Download or read book Design of Hardware Accelerators for Hierarchical Temporal Memory and Convolutional Neural Network written by Weifu Li and published by . This book was released on 2019 with total page 92 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Selected Topics in Intelligent Chips with Emerging Devices Circuits and Systems written by Alex James and published by CRC Press. This book was released on 2023-04-03 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: Memristors have provided a new direction of thinking for circuit designers to overcome the limits of scalability and for thinking of building systems beyond Moore’s law. Over the last decade, there has been a significant number of innovations in using memristors for building neural networks through analog computing, in-memory computing, and stochastic computing approaches. The emergence of intelligent integrated circuits is inevitable for the future of integrated circuit applications. This book provides a collection of talks conducted as part of the IEEE Seasonal School on Circuits and System, having a focus on Intelligence in Chip: Tomorrow of Integrated Circuits. Technical topics discussed in the book include: Edge of Chaos Theory Explains Complex Phenomena in Memristor Circuits Analog Memristive Computing Designing energy efficient neo-cortex system with on-device learning Integrated sensors Challenges and recent advances in NVM based Neuromorphic Computing ICs In-memory Computing (for deep learning) Deep learning with Spiking Neural Networks Computational Intelligence for Designing Integrated Circuits and Systems Neurochip Design, Modeling, and Applications

Download or read book Artificial Intelligence Applications and Reconfigurable Architectures written by Anuradha D. Thakare and published by John Wiley & Sons. This book was released on 2023-03-21 with total page 245 pages. Available in PDF, EPUB and Kindle. Book excerpt: ARTIFICIAL INTELLIGENCE APPLICATIONS and RECONFIGURABLE ARCHITECTURES The primary goal of this book is to present the design, implementation, and performance issues of AI applications and the suitability of the FPGA platform. This book covers the features of modern Field Programmable Gate Arrays (FPGA) devices, design techniques, and successful implementations pertaining to AI applications. It describes various hardware options available for AI applications, key advantages of FPGAs, and contemporary FPGA ICs with software support. The focus is on exploiting parallelism offered by FPGA to meet heavy computation requirements of AI as complete hardware implementation or customized hardware accelerators. This is a comprehensive textbook on the subject covering a broad array of topics like technological platforms for the implementation of AI, capabilities of FPGA, suppliers’ software tools and hardware boards, and discussion of implementations done by researchers to encourage the AI community to use and experiment with FPGA. Readers will benefit from reading this book because It serves all levels of students and researcher’s as it deals with the basics and minute details of Ecosystem Development Requirements for Intelligent applications with reconfigurable architectures whereas current competitors’ books are more suitable for understanding only reconfigurable architectures. It focuses on all aspects of machine learning accelerators for the design and development of intelligent applications and not on a single perspective such as only on reconfigurable architectures for IoT applications. It is the best solution for researchers to understand how to design and develop various AI, deep learning, and machine learning applications on the FPGA platform. It is the best solution for all types of learners to get complete knowledge of why reconfigurable architectures are important for implementing AI-ML applications with heavy computations. Audience Researchers, industrial experts, scientists, and postgraduate students who are working in the fields of computer engineering, electronics, and electrical engineering, especially those specializing in VLSI and embedded systems, FPGA, artificial intelligence, Internet of Things, and related multidisciplinary projects.

Download or read book Efficient Processing of Deep Neural Networks written by Vivienne Sze and published by Springer Nature. This book was released on 2022-05-31 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are currently widely used for many artificial intelligence (AI) applications, including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Therefore, techniques that enable efficient processing of deep neural networks to improve key metrics—such as energy-efficiency, throughput, and latency—without sacrificing accuracy or increasing hardware costs are critical to enabling the wide deployment of DNNs in AI systems. The book includes background on DNN processing; a description and taxonomy of hardware architectural approaches for designing DNN accelerators; key metrics for evaluating and comparing different designs; features of DNN processing that are amenable to hardware/algorithm co-design to improve energy efficiency and throughput; and opportunities for applying new technologies. Readers will find a structured introduction to the field as well as formalization and organization of key concepts from contemporary work that provide insights that may spark new ideas.

Download or read book Compute in memory Designs for Deep Neural Network and Combinatorial Optimization Problems Accelerators written by Shanshan Xie (Ph. D.) and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The unprecedented growth in Deep Neural Networks (DNN) model size has resulted into a massive amount of data movement from off-chip memory to on-chip processing cores in modern Machine Learning (ML) accelerators. Compute-In-Memory (CIM) designs performing analog DNN computations within a memory array along with peripheral data converter circuits, are being explored to mitigate this ‘Memory Wall’ bottleneck of latency and energy overheads. Embedded non-volatile magnetic [Wei et al. [2019]; Chih et al. [2020]; Dong et al. [2018]; Shih et al. [2019]], and resistive [Jain et al. [2019]; Chou et al. [2020]; Chang et al. [2014]; Lee et al. [2017]] as well as standalone Flash memories suffer from lower write-speeds and poor write-endurance and can’t be used for programmable accelerators requiring fast and frequent model updates. Similarly, cost-sensitive commodity DRAM (Dynamic Random Access Memory) can’t be leveraged for high-speed, custom CIM designs due to limited metal layers and dense floorplan constraints often leading to compute-near-memory designs limiting its throughput benefits [Aga et al. [2019]]. Among the prevalent semiconductor memories, eDRAM (embedded-DRAM) which integrates the DRAM bitcell monolithically along with high-performance logic transistors and interconnects can enable custom CIM designs by offering the densest embedded bitcell, low pJ/bit access energy, high-endurance, high-performance, and high-bandwidth; all desired attributes for ML accelerators [Fredeman et al. [2015]; Berry et al. [2020]]. Yet, eDRAM has been used in niche applications due to its high cost/bit, low retention time, and high noise sensitivity. On the DNN algorithms front, the landscape is rapidly changing with the adoption of 8-bit integer arithmetic for both DNN inference and training algorithms [Jouppi et al. [2017]; Yang et al. [2020]]. These reduced bit-width computations are extremely conducive for CIM designs which have shown promising results for integer arithmetic [Biswas and Chandrakasan [2018]; Gonugondla et al. [2018a]; Zhang et al. [2017]; Si et al. [2019]; Yang et al. [2019]; Khwa et al. [2018]; Chen et al. [2019]; Dong et al. [2020]; Valavi et al. [2019]; Dong et al. [2017]; Jiang et al. [2019]; Yin et al. [2020]]. Thus, high cost/bit of eDRAM can now be amortized by repurposing existing eDRAM in high-end processors for enabling CIM circuits. Despite the potential of eDRAM technology and the progress in DNN integer arithmetic, no hardware demonstration for eDRAM-based CIM design has been reported so far. Therefore, in this dissertation, the first project explores the compute-in-memory concept with the dense 1T1C eDRAM bitcells as charge domain circuits for convolution neural network (CNN) multiply-accumulation-averaging (MAV) computation. This method minimizes area overhead by leveraging existing 1T1C eDRAM columns to construct an adaptive data converter, dot-product, averaging, pooling, and ReLU activation on the memory array. The second project presents a leakage and read bitline (RBL) swing-aware compute-in-memory (CIM) design leveraging a promising high-density gain-cell embedded DRAM bitcell and the intrinsic RBL capacitors to perform CIM computations within the limited RBL swing available in a 2T1C eDRAM. The CIM D/A converters (DAC) are realized intrinsically with variable RBL precharge voltage levels. A/D converters (ADC) are realized using Schmitt Triggers (ST) as compact and reconfigurable Flash comparators. Similar to machine learning applications, combinatorial optimation problems (COP) also require data-intensive computations, which are naturally suitable for adopting the compute-in-memory concept as well. Combinatorial optimization problems find many real-world social and industrial data-intensive computing applications. Examples include optimization of mRNA sequences for COVID-19 vaccines [Leppek et al. [2021]; Pardi et al. [2018]], semiconductor supply-chains [Crama [1997]; Kempf [2004]], and financial index tracking [Benidis et al. [2018]], to name a few. Such COPs are predominantly NP-hard [Yuqi Su and Kim [2020]], and performing an exhaustive brute force search becomes untenable as the COP size increases. An efficient way to solve COPs is to let nature perform the exhaustive search in the physical world using the Ising model, which can map many types of COPs [Lucas [2014]], The Ising model describes spin dynamics in a ferromagnetic [Peierls [1936]], wherein spins naturally orient to achieve the lowest ensemble energy state of the Ising model, representing the optimal COP solution [Yoshimura et al. [2015]]. Therefore, in order to accelerate the COP computations, the third project focuses on implementing analog compute-in-memory techniques for Ising computation to eliminate unnecessary data movement and to reduce energy costs. The COPs can be mapped into a generic Ising model framework, and the computations are performed directly on the bitlines. Spin updates are performed locally using the existing sense amplifier in the peripheral circuits and the write-after-read mechanism in the memory array controller. Beyond that, the fourth project explores the CIM designs for solving Boolean Satisfiability (SAT) problems, which s a non-deterministic polynomial time (NP)-complete problems with many practical and industrial data-intensive applications. An all-digital SAT solver, called Snap-SAT, is presented to accelerate the iterative computations using the static random-access memory (SRAM) array to reduce the frequent memory access and minimize the hardware implementation cost. This design demonstrates a promising, fast, reliable, reconfigurable, and scalable compute-in-memory design for solving and accelerating large-scale hard SAT problems, suggesting its potential for solving time-critical SAT problems in real-life applications (e.g., defense, vaccine development, etc.)

Download or read book Scalable Digital Architecture of Hierarchical Temporal Memory Spatial Pooler written by Sadhvi Praveen and published by . This book was released on 2017 with total page 76 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Hierarchical Temporal memory is an unsupervised machine learning algorithm. Inspired by the structural and functional properties of the human brain, it is capable of processing spatio-temporal signals which are used for data storage and predictions. The algorithm is composed of two main components; the Spatial Pooler and the Temporal Memory. The spatial pooler produces a sparse distribution representation for the given pattern. These generalized representations are used by the temporal memory to make predictions. Therefore, it is important to ensure that more generalized sparse distribution representations are obtained for the spatio-temporal data patterns. This work presents the digital design of spatial pooler implementation for an existing mathematical algorithm along with an analysis of its scalability for the target FPGA device. The digital design is implemented in two ways; Conventional and Parallel architectures. The architectures are compared in terms of speedup, area and power consumption. Based on the analysis of results, it is seen that the parallel approach is more efficient in terms of speed and power, with a negligible increase in device utilization. The spatial pooler design is evaluated against the standard MNIST dataset, obtaining up to 90% and 88% classication accuracy for the train and test data, respectively. Additionally, the designs are tested on the MNIST dataset, in the presence of noise, to determine its robustness. Fluctuations of up to 10% of the peak accuracy are observed during classication, and are noted in the classication accuracy plots for the dataset with noise. The design is synthesized for the Xilinx Virtex 7 family with a total power consumption of up to 260 mW."--Abstract.

Download or read book Hardware Accelerators for Machine Learning From 3D Manycore to Processing in Memory Architectures written by Aqeeb Iqbal Arka and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big data applications such as - deep learning and graph analytics require hardware platforms that are energy-efficient yet computationally powerful. 3D manycore architectures are the key to efficiently executing such compute- and data-intensive applications. Through silicon via (TSV)-based 3D manycore system is a promising solution in this direction as it enables integration of disparate heterogeneous computing cores on a single system. Recent industry trends show the viability of 3D integration in real products (e.g., Intel Lakefield SoC Architecture, the AMD Radeon R9 Fury X graphics card, and Xilinx Virtex-7 2000T/H580T, etc.). However, the achievable performance of conventional through-silicon-via (TSV)-based 3D systems is ultimately bottlenecked by the horizontal wires (wires in each planar die). Moreover, current TSV 3D architectures suffer from thermal limitations. Hence, TSV-based architectures do not realize the full potential of 3D integration. Monolithic 3D (M3D) integration, a breakthrough technology to achieve "More Moore and More Than Moore," and opens up the possibility of designing cores and associated network routers using multiple layers by utilizing monolithic inter-tier vias (MIVs) and hence, reducing the effective wire length. Compared to TSV-based 3D ICs, M3D offers the "true" benefits of vertical dimension for system integration: the size of a MIV used in M3D is over 100x smaller than a TSV. However, designing these new architectures often involves optimizingmultiple conflicting objectives (e.g., performance, thermal, etc.) due to thepresence of a mix of computing elements and communication methodologies; each with a different requirement for high performance. To overcome the difficult optimization challenges due to the large design space and complex interactions among the heterogeneous components (CPU, GPU, Last Level Cache, etc.) in an M3D-based manycore chip, Machine Learning algorithms can be explored as a promising solution to this problem and. The first part of this dissertation focuses on the design of high-performance and energy-efficient architectures for big-data applications, enabled by M3D vertical integration and data-driven machine learning algorithms. As an example, we consider heterogeneous manycore architectures with CPUs, GPUs, and Cache as the choice of hardware platform in this part of the work. The disparate nature of these processing elements introduces conflicting design requirements that need to be satisfied simultaneously. Moreover, the on-chip traffic pattern exhibited by different big-data applications (like many-to-few-to-many in CPU/GPU-based manycore architectures) need to be incorporated in the design process for optimal power-performance trade-off. In this dissertation, we first design a M3D-enabled heterogeneous manycore architecture and we demonstrate the efficacy of machine learning algorithms for efficiently exploring a large design space. For large design space exploration problems, the proposed machine learning algorithm can find good solutions in significantly less amount of time than exiting state-of-the-art counterparts. However, the M3D-enabled heterogeneous manycore architecture is still limited by the inherent memory bandwidth bottlenecks of traditional von-Neumann architectures. As a result, later in this dissertation, we focus on Processing-in-Memory (PIM) architectures tailor-made to accelerate deep learning applications such as Graph Neural Networks (GNNs) as such architectures can achieve massive data parallelism and do not suffer from memory bandwidth-related issues. We choose GNNs as an example workload as GNNs are more complex compared to traditional deep learning applications as they simultaneously exhibit attributes of both deep learning and graph computations. Hence, it is both compute- and data-intensive in nature. The high amount of data movement required by GNN computation poses a challenge to conventional von-Neuman architectures (such as CPUs, GPUs, and heterogeneous system-on-chips (SoCs)) as they have limited memory bandwidth. Hence, we propose the use of PIM-based non-volatile memory such as Resistive Random Access Memory (ReRAM). We leverage the efficient matrix operations enabled by ReRAMs and design manycore architectures that can facilitate the unique computation and communication needs of large-scale GNN training. We then exploit various techniques such as regularization methods to further accelerate GNN training ReRAM-based manycore systems. Finally, we streamline the GNN training process by reducing the amount of redundant information in both the GNN model and the input graph.Overall, this work focuses on the design challenges of high-performance and energy-efficient manycore architectures for machine learning applications. We propose novel architectures that use M3D or ReRAM-based PIM architectures to accelerate such applications. Moreover, we focus on hardware/software co-design to ensure the best possible performance.

Download or read book Deep Learning Classifiers with Memristive Networks written by Alex Pappachen James and published by Springer. This book was released on 2019-04-08 with total page 213 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces readers to the fundamentals of deep neural network architectures, with a special emphasis on memristor circuits and systems. At first, the book offers an overview of neuro-memristive systems, including memristor devices, models, and theory, as well as an introduction to deep learning neural networks such as multi-layer networks, convolution neural networks, hierarchical temporal memory, and long short term memories, and deep neuro-fuzzy networks. It then focuses on the design of these neural networks using memristor crossbar architectures in detail. The book integrates the theory with various applications of neuro-memristive circuits and systems. It provides an introductory tutorial on a range of issues in the design, evaluation techniques, and implementations of different deep neural network architectures with memristors.

Download or read book Efficient Inference Acceleration written by Michael Alan Mishkin and published by . This book was released on 2019 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: Forward progress in computing technology is expected to involve high degrees of heterogeneity and specialization. Emerging applications integrating neural networks are becoming more common and as a result development of specialized hardware designed for acceleration of neural networks is increasingly economical. As Moore's law wanes and applications utilizing neural networks benefit from high-performance and low-power execution provided by widely available specialized hardware, algorithms using neural networks are poised to continue to outpace alternative approaches. This dissertation explores the design space of neural network inference accelerators, spanning from monolithic systolic arrays with off-chip DRAMs for weight storage to tiled matrix-vector units with tightly coupled on-chip weight storage to supply high bandwidth weights without dependence on off-chip memory, targeting efficient microarchitectural techniques and neural network inference sequencing schemes, identifying three key design points of interest. The first is a monolithic systolic array based accelerator where pipeline depths are reduced in order to eliminate clocked element overheads. These optimizations primarily target energy-efficiency but also improve performance subject to bandwidth limitations. The accelerator includes weight permutation considerations required to better support processing convolutional layers on wide arrays using scheduling policies that preserve temporal locality of weight sub-matrices. The second accelerator uses codebook quantization for both weights and activations to reduce power associated with both on-chip communication and synapse calculation. Codebook based quantization and dequantization are tightly integrated into the accelerator data-path enabling the bulk of on-chip communication to remain in the quantized format. Training experiments are presented to provide insight into training techniques for inference accelerators utilizing codebook quantization of both activations and weights. The third accelerator design considers communication power reduction within a tiled accelerator using temporally coded interconnects for both activations and weights. Tolerance for the latency of the temporal codes within neural network accelerators is achieved by scheduling schemes that facilitate reuse of temporally communicated values and buffer capacities provisioned to support these schedules. Within the accelerator with temporally coded links, these adverse effects amount to performance degradations rather than high power consumption.

Download or read book Exploiting Data Characteristics in The Design of Accelerators for Deep Learning written by Patrick H. Judd and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The recent "Cambrian explosion" of Deep Learning (DL) algorithms in concert with the end of Moore's Law and Dennard Scaling has spurred interest in the design of custom hardware accelerators for DL algorithms. While DL has progressed quickly thanks in part to the abundance of efficient parallel computation provided by General Purpose Graphics Processing Units, newer DL algorithms demand even higher levels of compute density and efficiency. Furthermore, applications of DL in the mobile and embedded domains demand the energy efficiency of special purpose hardware. DL algorithms are dominated by large matrix-vector product computations, making them ideal targets for wide Single Instruction Multiple Data architectures. For the most part, efficiently mapping the structure of these computations to hardware is straightforward. Building on such designs, this thesis examines the data characteristics of these computations and proposes hardware modifications to exploit them for performance and energy efficiency. Specifically, this thesis examines the sparsity and precision requirements of Deep Convolutional Neural Networks, which comprise multiple layers of matrix-vector product computations. We propose a profiling method to find per layer reduced precision configurations while maintaining high classification accuracy. Following this, we propose three accelerator designs that build on top of the state-of-the-art DaDianNao accelerator. 1) Proteus exploits the reduced precision profiles by adding a light weight memory compression layer, saving energy in memory access and communication, and enabling larger networks in a fixed memory budget. 2) Cnvlutin exploits the presence of zero, and near zero, values in the inter-layer data by applying sparse compression to the data stream while maintain efficient utilization of the wide memory and compute structure of the SIMD accelerator. 3) Stripes exploits the reduced precision profiles for performance by processing data bit-serially, compensating for serial latency by exploiting the abundant parallelism in the convolution operation. All three designs exploit approximation, in terms of reduced precision and computation skipping to improve energy efficiency and/or performance while maintaining high classification accuracy. By approximating more aggressively, these designs can also dynamically trade-off accuracy for further improvements in performance and energy.

Download or read book Handbook of Evolutionary Machine Learning written by Wolfgang Banzhaf and published by Springer Nature. This book was released on 2023-11-01 with total page 764 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book, written by leading international researchers of evolutionary approaches to machine learning, explores various ways evolution can address machine learning problems and improve current methods of machine learning. Topics in this book are organized into five parts. The first part introduces some fundamental concepts and overviews of evolutionary approaches to the three different classes of learning employed in machine learning. The second addresses the use of evolutionary computation as a machine learning technique describing methodologic improvements for evolutionary clustering, classification, regression, and ensemble learning. The third part explores the connection between evolution and neural networks, in particular the connection to deep learning, generative and adversarial models as well as the exciting potential of evolution with large language models. The fourth part focuses on the use of evolutionary computation for supporting machine learning methods. This includes methodological developments for evolutionary data preparation, model parametrization, design, and validation. The final part covers several chapters on applications in medicine, robotics, science, finance, and other disciplines. Readers find reviews of application areas and can discover large-scale, real-world applications of evolutionary machine learning to a variety of problem domains. This book will serve as an essential reference for researchers, postgraduate students, practitioners in industry and all those interested in evolutionary approaches to machine learning.

Download or read book A Novel FPGA Implementation of Hierarchical Temporal Memory Spatial Pooler written by Paul Jeffrey Mitchell and published by . This book was released on 2018 with total page 78 pages. Available in PDF, EPUB and Kindle. Book excerpt: "There is currently a strong focus across the technological landscape to create machines capable of performing complex, objective based tasks in a manner similar to, or superior to a human. Many of the methods being explored in the machine intelligence space require large sets of labeled data to first train, and then classify inputs. Hierarchical Temporal Memory (HTM) is a biologically inspired machine intelligence framework which aims to classify and interpret streaming unlabeled data, without supervision, and be able to detect anomalies in such data. In software HTM models, increasing the number of "columns" or processing elements to the levels required to make meaningful predictions in complex data can be prohibitive to analyzing in real time. There exists a need to improve the throughput of such systems. HTMs require large amounts of data available to be accessed randomly, and then processed independently. FPGAs provide a reconfigurable, and easily scalable platform ideal for these types of operations. One of the two main components of the HTM architecture is the "spatial pooler". This thesis explores a novel hardware implementation of an HTM spatial pooler, with a "boosting" algorithm to increase homeostasis, and a novel classification algorithm to interpret input data in real time. This implementation shows a significant speedup in data processing, and provides a framework to scale the implementation based on the available hardware resources of the FPGA."--Boise State University ScholarWorks.

Download or read book Design and Performance Analysis of Hardware Accelerator for Deep Neural Network in Heterogeneous Platform written by Md Syadus Sefat and published by . This book was released on 2018 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis describes a new flexible approach to implementing energy-efficient DNN accelerator on FPGAs. Our design leverages the Coherent Accelerator Processor Interface (CAPI) which provides a cache-coherent view of system memory to attached accelerators. Computational kernels are accelerated on a CAPI-supported Kintex FPGA board. Our implementation bypasses the need for device driver code and significantly reduces the communication and I/O transfer overhead. To improve the performance of the entire application, we propose a collaborative model of execution in which the control of the data flow within the accelerator is kept independent, freeing-up CPU cores to work on other parts of the application. For further performance enhancements, we propose a technique to exploit data locality in the cache, situated in the CAPI Power Service Layer (PSL). Finally, we develop a resource-conscious implementation for more efficient utilization of resources and improved scalability. Compared with the previous work, our architecture achieves both improved performance and better power efficiency.

Download or read book Energy efficient ASIC Accelerators for Machine deep Learning Algorithms written by Minkyu Kim and published by . This book was released on 2019 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this work, to reduce computation without accuracy degradation, an energy-efficient deep convolutional neural network (DCNN) accelerator is proposed based on a novel conditional computing scheme and integrates convolution with subsequent max-pooling operations. This way, the total number of bit-wise convolutions could be reduced by ~2x, without affecting the output feature values. This work also has been developing an optimized dataflow that exploits sparsity, maximizes data re-use and minimizes off-chip memory access, which can improve upon existing hardware works. The total off-chip memory access can be saved by 2.12x. Preliminary results of the proposed DCNN accelerator achieved a peak 7.35 TOPS/W for VGG-16 by post-layout simulation results in 40nm. A number of recent efforts have attempted to design custom inference engine based on various approaches, including the systolic architecture, near memory processing, and in-meomry computing concept. This work evaluates a comprehensive comparison of these various approaches in a unified framework. This work also presents the proposed energy-efficient in-memory computing accelerator for deep neural networks (DNNs) by integrating many instances of in-memory computing macros with an ensemble of peripheral digital circuits, which supports configurable multibit activations and large-scale DNNs seamlessly while substantially improving the chip-level energy efficiency. Proposed accelerator is fully designed in 65nm, demonstrating ultralow

Download or read book Compact and Fast Machine Learning Accelerator for IoT Devices written by Hantao Huang and published by Springer. This book was released on 2018-12-07 with total page 149 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the latest techniques for machine learning based data analytics on IoT edge devices. A comprehensive literature review on neural network compression and machine learning accelerator is presented from both algorithm level optimization and hardware architecture optimization. Coverage focuses on shallow and deep neural network with real applications on smart buildings. The authors also discuss hardware architecture design with coverage focusing on both CMOS based computing systems and the new emerging Resistive Random-Access Memory (RRAM) based systems. Detailed case studies such as indoor positioning, energy management and intrusion detection are also presented for smart buildings.

Download or read book Deep Learning for Computer Architects written by Brandon Reagen and published by Springer Nature. This book was released on 2022-05-31 with total page 109 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning, and specifically deep learning, has been hugely disruptive in many fields of computer science. The success of deep learning techniques in solving notoriously difficult classification and regression problems has resulted in their rapid adoption in solving real-world problems. The emergence of deep learning is widely attributed to a virtuous cycle whereby fundamental advancements in training deeper models were enabled by the availability of massive datasets and high-performance computer hardware. This text serves as a primer for computer architects in a new and rapidly evolving field. We review how machine learning has evolved since its inception in the 1960s and track the key developments leading up to the emergence of the powerful deep learning techniques that emerged in the last decade. Next we review representative workloads, including the most commonly used datasets and seminal networks across a variety of domains. In addition to discussing the workloads themselves, we also detail the most popular deep learning tools and show how aspiring practitioners can use the tools with the workloads to characterize and optimize DNNs. The remainder of the book is dedicated to the design and optimization of hardware and architectures for machine learning. As high-performance hardware was so instrumental in the success of machine learning becoming a practical solution, this chapter recounts a variety of optimizations proposed recently to further improve future designs. Finally, we present a review of recent research published in the area as well as a taxonomy to help readers understand how various contributions fall in context.

Download or read book TinyML written by Pete Warden and published by O'Reilly Media. This book was released on 2019-12-16 with total page 504 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size—small enough to run on a microcontroller. With this practical book you’ll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices. Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through creating a series of TinyML projects, step-by-step. No machine learning or microcontroller experience is necessary. Build a speech recognizer, a camera that detects people, and a magic wand that responds to gestures Work with Arduino and ultra-low-power microcontrollers Learn the essentials of ML and how to train your own models Train models to understand audio, image, and accelerometer data Explore TensorFlow Lite for Microcontrollers, Google’s toolkit for TinyML Debug applications and provide safeguards for privacy and security Optimize latency, energy usage, and model and binary size