Author: Ruo Long LianPublisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
A Framework for FPGA-based Acceleration of Neural Network Inference with Limited Numerical Precision Via High-level Synthesis with Streaming Functionality
Author: Ruo Long LianCaffeinated FPGAs
Author: Roberto DiCeccoPublisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
This thesis presents a framework for performing training and inference of Convolutional Neural Networks (CNNs) with reduced precision floating-point arithmetic. This work aims to provide a means for FPGA and machine learning researchers to use the customizability of FPGAs to explore the precision requirements of training CNNs with an open-source framework. This is accomplished through the creation of a High-Level Synthesis library with a Custom Precision Floating-Point data type that is configurable in both exponent and mantissa widths, with several standard operators and rounding modes supported. With this library a FPGA CNN Training Engine (FCTE) has been created along with a FPGA CNN framework FPGA Caffe, which is built on Caffe. FCTE has a peak performance of approximately 350 GFLOPs, and has been used to show that a mantissa width of 5 and exponent width of 6 is sufficient for training several models targeting the MNIST and CIFAR-10 datasets.
Hardware Accelerators in Data Centers
Author: Christoforos KachrisPublisher: Springer
ISBN: 3319927922
Category : Technology & Engineering
Languages : en
Pages : 280
Book Description
This book provides readers with an overview of the architectures, programming frameworks, and hardware accelerators for typical cloud computing applications in data centers. The authors present the most recent and promising solutions, using hardware accelerators to provide high throughput, reduced latency and higher energy efficiency compared to current servers based on commodity processors. Readers will benefit from state-of-the-art information regarding application requirements in contemporary data centers, computational complexity of typical tasks in cloud computing, and a programming framework for the efficient utilization of the hardware accelerators.
Framework for Mapping Convolutional Neural Networks on FPGAs
Author: Masoud ShahshahaniPublisher:
ISBN:
Category : Artificial intelligence
Languages : en
Pages : 0
Book Description
Artificial Intelligence (AI) applications are on the rise. Recent advances in machine learning and deep learning have created various applications for medicine/healthcare, financial markets, security, entertainment, and social sciences. Deep Learning, especially, has demonstrated tremendous opportunities in computer vision, autonomous driving, natural language processing, and many more. Deep learning allows machines to solve complex problems using Artificial Neural Networks (ANNs), and the learning itself can be supervised or semisupervised. Multilayered artificial neural networks are called Deep Neural Networks (DNNs). These deep computational models are composed of multiple sequentially processing layers that help learn the representations within a given data set. Convolutional Neural Networks (CNN) are a particular class of deep networks that use convolution to extract features from (usually a time-domain or frequency-domain) data and then use the extracted features to classify that data for final inferencing. Several software tools and frameworks are available to facilitate the deep learning community with the fast development and high-performance execution of DNNs. Tool flows, such as PyTorch, Caffe, Theano, and TensorFlow, aim to increase the productivity of CNN software developers by providing a pathway for implementing deep networks on high-performance multi-core CPUs, GPUs, and DSPs. GPUs, especially, provide easy access to floating point operations and also allow very high memory bandwidths. Some of the latest Nvidia GPUs (Nvidia GeForce RTX2080) consume as much as 300 watts of power. Excessive power dissipation can make GPUs an unfavorable candidate for implementing CNNs for a variety of applications. Field Programmable Gate Arrays (FPGAs) provide a high degree of customized parallelization and offer far superior performance per watt. We believe that FPGA-based accelerators are ideal platforms for implementing Convolutional Neural Networks for computer vision and related applications. Software engineers with minimal hardware design skills demand tremendous support within the tool-flows, and FPGA vendors are fully embracing new methodologies like high-level synthesis, where the designs can be described as a program written in languages like C/C++. However, commercial FPGAs are resource-scarce, the CNN mapping design space is enormous, and efficient mapping of CNN can quickly become a challenging task. The requirement of FPGA resources, latency, and power is affected by many parameters, including the CNN architecture and the level of computational parallelism. In practice, a software designer first explores various CNN architectures in software to improve architecture0́9s validation accuracy. Once an architecture has been finalized, the designer ports the architecture design to FPGA for inference acceleration. The mapping process undergoes performance optimization by tweaking many design-related parameters during the design space exploration and changing the operating frequencies. The entire process is highly time-consuming. This dissertation describes a fully automated end-to-end design framework for implementing CNNs on FPGAs. The framework allows a designer to express the CNNs in commonly preferred Python language descriptions and provides a guided tool flow to generate a custom Intellectual Property (IP) block. In addition, the framework allows easy and complete exploration for selecting final design implementations based on optimization parameters that include Performance, Power, and Area (PPA).
FPGA Implementations of Neural Networks
Author: Amos R. OmondiPublisher: Springer Science & Business Media
ISBN: 0387284877
Category : Technology & Engineering
Languages : en
Pages : 365
Book Description
During the 1980s and early 1990s there was signi?cant work in the design and implementation of hardware neurocomputers. Nevertheless, most of these efforts may be judged to have been unsuccessful: at no time have have ha- ware neurocomputers been in wide use. This lack of success may be largely attributed to the fact that earlier work was almost entirely aimed at developing custom neurocomputers, based on ASIC technology, but for such niche - eas this technology was never suf?ciently developed or competitive enough to justify large-scale adoption. On the other hand, gate-arrays of the period m- tioned were never large enough nor fast enough for serious arti?cial-neur- network (ANN) applications. But technology has now improved: the capacity and performance of current FPGAs are such that they present a much more realistic alternative. Consequently neurocomputers based on FPGAs are now a much more practical proposition than they have been in the past. This book summarizes some work towards this goal and consists of 12 papers that were selected, after review, from a number of submissions. The book is nominally divided into three parts: Chapters 1 through 4 deal with foundational issues; Chapters 5 through 11 deal with a variety of implementations; and Chapter 12 looks at the lessons learned from a large-scale project and also reconsiders design issues in light of current and future technology.
TinyML
Author: Pete WardenPublisher: O'Reilly Media
ISBN: 1492052019
Category : Computers
Languages : en
Pages : 504
Book Description
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
An OpenCL Framework for Real-time Inference of Next-generation Convolutional Neural Networks on FPGAs
Author: Sachin KumawatPublisher:
ISBN: 9780355764413
Category :
Languages : en
Pages :
Book Description
Modern Convolutional Neural Networks (CNNs) consist of billions of multiplications and additions which require the use of parallel computing units such as GPUs, FPGAs and other DSP processors. Consequently, General Purpose GPU (GPGPU) computing has taken this field by storm. At the same time, there has been an increased interest in FPGA based acceleration of CNN inference. In this work, we present FICaffe, a framework for FPGA-based Inference with Caffe, which provides a complete automated generation and mapping of CNN accelerators on FPGAs. We target applications with critical latency requirements and design high processing efficiency accelerators for CNNs. The architecture is structured in a highly concurrent OpenCL library, which enables High Level Synthesis tools to effectively exploit data, task and pipeline parallelism. We propose a unified memory model, that drives exploration of optimal design by matching on-chip and off-chip memory bandwidths available on FPGA platforms. We also identify origins of all clock cycle stalls and overheads inherent to CNN acceleration designs and provide a detailed model to accurately predict the runtime latency with less than 4% error against on-board tests. Furthermore, with FICaffe we provide support for cross-network synthesis, such that it is possible to processes a variety of CNNs, with reasonable efficiency, without long re-compilation hours. FICaffe is integrated with the popular deep learning framework Caffe, and is deployable to a wide variety of CNNs. FICaffe's efficacy is shown by mapping to a 28nm Stratix V GXA7 chip, and both network specific and cross-network performance are reported for AlexNet, VGG, SqueezeNet and GoogLeNet. We show a processing efficiency of 95.8% for the widely-reported VGG benchmark, which outperforms prior work. FICaffe also achieves more than 2X speedup on Stratix V GXA7 compared with the best published results on this chip, to the best of our knowledge.
High-Performance Computing Using FPGAs
Author: Wim VanderbauwhedePublisher: Springer Science & Business Media
ISBN: 1461417910
Category : Technology & Engineering
Languages : en
Pages : 798
Book Description
High-Performance Computing using FPGA covers the area of high performance reconfigurable computing (HPRC). This book provides an overview of architectures, tools and applications for High-Performance Reconfigurable Computing (HPRC). FPGAs offer very high I/O bandwidth and fine-grained, custom and flexible parallelism and with the ever-increasing computational needs coupled with the frequency/power wall, the increasing maturity and capabilities of FPGAs, and the advent of multicore processors which has caused the acceptance of parallel computational models. The Part on architectures will introduce different FPGA-based HPC platforms: attached co-processor HPRC architectures such as the CHREC’s Novo-G and EPCC’s Maxwell systems; tightly coupled HRPC architectures, e.g. the Convey hybrid-core computer; reconfigurably networked HPRC architectures, e.g. the QPACE system, and standalone HPRC architectures such as EPFL’s CONFETTI system. The Part on Tools will focus on high-level programming approaches for HPRC, with chapters on C-to-Gate tools (such as Impulse-C, AutoESL, Handel-C, MORA-C++); Graphical tools (MATLAB-Simulink, NI LabVIEW); Domain-specific languages, languages for heterogeneous computing(for example OpenCL, Microsoft’s Kiwi and Alchemy projects). The part on Applications will present case from several application domains where HPRC has been used successfully, such as Bioinformatics and Computational Biology; Financial Computing; Stencil computations; Information retrieval; Lattice QCD; Astrophysics simulations; Weather and climate modeling.
FPGA Implementation of Reduced Precision Convolutional Neural Networks
Author: Muhammad Mohid NabilPublisher:
ISBN:
Category : Convolutions (Mathematics)
Languages : en
Pages :
Book Description
With the improvement in processing systems, machine learning applications are finding widespread use in almost all sectors of technology. Image recognition is one application of machine learning which has become widely popular with various architectures and systems aimed at improving recognition performance. With classification accuracy now approaching saturation point, many researchers are now focusing on resource and energy efficiency. With the increased demand for learning applications in embedded devices, it is of paramount importance to optimize power and energy consumption to increase utility in these low power embedded systems. In recent months, reduced precision neural networks have caught the attention of some researchers. Reduced data width deep nets offer the potential of saving valuable resources on hardware platforms. In turn, these hardware platforms such as Field Programmable Gate Arrays (FPGAs) offer the potential of a low power system with massive parallelism increasing throughput and performance. In this research, we explore the implementations of a deep learning architecture on FPGA in the presence of resource and energy constraints. We study reduced precision neural networks and implement one such architecture as a proof of concept. We focus on binarized convolutional neural network and its implementation on FPGAs. Binarized convolutional nets have displayed a classification accuracy of up to 88% with some smaller image sets such as CIFAR-10. This number is on the rise with some of the new architectures. We study the tradeoff between architecture depth and its impact on accuracy to get a better understanding of the convolutional layers and their impact on the overall performance. This is done from a hardware perspective giving us better insight enabling better resource allocation on FPGA fabric. Zynq ZCU-102 has been used for accelerator implementation. High level synthesis tool (Vivado HLS) from Xilinx is used for CNN definition on FPGA fabric.
NengoFPGA
Author: Benjamin MorcosPublisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
Low-power, high-speed neural networks are critical for providing deployable embedded AI applications at the edge. We describe a Xilinx FPGA implementation of Neural Engineering Framework (NEF) networks with online learning that outperforms mobile Nvidia GPU implementations by an order of magnitude or more. Specifically, we provide an embedded Python-capable PYNQ FPGA implementation supported with a Xilinx Vivado High-Level Synthesis (HLS) workflow that allows sub-millisecond implementation of adaptive neural networks with low-latency, direct I/O access to the physical world. The outcome of this work is NengoFPGA, a seamless and user-friendly extension to the neural compiler Python package Nengo. To reduce memory requirements and improve performance we tune the precision of the different intermediate variables in the code to achieve competitive absolute accuracy against slower and larger floating-point reference designs. The online learning component of the neural network exploits immediate feedback to adjust the network weights to best support a given arithmetic precision. As the space of possible design configurations of such quantized networks is vast and is subject to a target accuracy constraint, we use the Hyperopt hyper-parameter tuning tool instead of manual search to find Pareto optimal designs. Specifically, we are able to generate the optimized designs in under 500 short iterations of Vivado HLS C synthesis before running the complete Vivado place-and-route phase on that subset, a much longer process not conducive to rapid exploration. For neural network populations of 64-4096 neurons and 1-8 representational dimensions our optimized FPGA implementation generated by Hyperopt has a speedup of 10-484× over a competing cuBLAS implementation on the Jetson TX1 GPU while using 2.4-9.5× less power. Our speedups are a result of HLS-specific reformulation (15× improvement), precision adaptation (3× improvement), and low-latency direct I/O access (1000× improvement).