Specialized hardware for deep learning will unleash innovationBen Lorica
第六章 EIE-用于稀疏神经网络的高效推理引擎 目测和发在ISCA2016的论文EIE: Efficient Inference Engine on Compressed Deep Neural Network内容一致,补了一些图。是一个神经网络加速器,用硬件加速训练好的网络模型。 6.1 Introduction 为了评估EIE性能,为其建立了行为级描述和RTL级模型,并对RTL模型进行了综合和布局布线...
As deep neural network (DNN) models grow ever-larger, they can achieve higher accuracy and solve more complex problems. This trend has been enabled by an increase in available compute power; however, efforts to continue to scale electronic processors are
首先,我关注的是今年的ISSCC会议上的Deep Learning Processor这个专题,对相关的论文做了一些导读。这些论文对于希望在Edge端应用里追求更高的效率的读者,应该有一些借鉴意义:14.1 G. Desoli, STMicroelectronics, Cornaredo, Italy, "A 2.9TOPS/W Deep Convolutional Neural Network SoC in FD-SOI 28nm for Intelligen...
3. Hardware for Efficient Inference 这个方向各种硬件的共同目的是减少内存的读取(minimize memory access)。硬件需要能用压缩过的神经网络做预测。 EIE(Efficient Inference Engine)(Han et al. ISCA 2016):稀疏权重(扔掉为0的权重)、稀疏激活值(扔掉为0的激活值)、Weight Sharing(4-bit)。
SheffieldUnited KingdomArtificial IntelligenceComputer ArchitecturesComputer VisionElectronic EngineeringMachine Learning About the Project Artificial Intelligence (AI) is becoming the default choice for the many of the applications in the industries such as image processing and pattern recognition. As a result...
Hardware Exploration for Varying FPGA Sizes 如图一所示,Architectural knobs包括GEMM硬件内在形状,数据类型等,Circuit knobs包括硬件流水线在较高频率和锁相环频率关闭时序的程度。这些定制旋钮定义了一个包含100到1000个独立设计的硬件设计空间。对其进行exhaustively explored来寻找最优解 具体搜索方法如下: 使用简单的FP...
全文分析:《Fire-Flyer AI-HPC: A Cost-Effective Software-Hardware Co-Design for Deep Learning》 1. 论文概述 本文介绍了Fire-Flyer AI-HPC架构,这是一种软硬件协同设计的深度学习高性能计算(AI-HPC)系统,旨在以更低成本实现大规模深度学习(DL)和大语言模型(LLMs)的训练。研究团队部署了10,000 台 PCIe ...
DeepBench attempts to answer the question, "Which hardware provides the best performance on the basic operations used for deep neural networks?". We specify these operations at a low level, suitable for use in hardware simulators for groups building new processors targeted at deep learning. DeepBenc...
The rapid progress in Deep Learning (DL) and Large Language Models (LLMs) has exponentially increased demands of computational power and bandwidth. This, combined with the high costs of faster computing chips and interconnects, has significantly inflated High Performance Computing (HPC) construction ...