.png){kind=icon}
返回
Weight-Oriented Approximation for Energy-Efficient Neural Network Inference Accelerators
.png){kind=icon}
PRE
AI
DOI：10.1109/TCSI.2020.3019460.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Current research in the area of Neural Networks (NN) has resulted in performance advancements for a variety of complex problems. Especially, embedded system applications rely more and more on the utilization of convolutional NNs to provide services such as image/audio classification and object detection. The core arithmetic computation performed during NN inference is the multiply-accumulate (MAC) operation. In order to meet tighter and tighter throughput constraints, NN accelerators integrate thousands of MAC units resulting in a significant increase in power consumption. Approximate computing is established as a design alternative to improve the efficiency of computing systems by trading computational accuracy for high energy savings. In this work, we bring approximate computing principles and NN inference together by designing NN specific approximate multipliers that feature multiple accuracy levels at run-time. We propose a time-efficient automated framework for mapping the NN weights to the accuracy levels of the approximate reconfigurable accelerator. The proposed weight-oriented approximation mapping is able to satisfy tight accuracy loss thresholds, while significantly reducing energy consumption without any need for intensive NN retraining. Our approach is evaluated against several NNs demonstrating that it delivers high energy savings (17.8% on average) with a minimal loss in inference accuracy (0.5%).
Keyword:
Artificial neural networks
Approximate computing
Energy consumption
Convolution
Hardware
Embedded systems
Approximate computing
neural network inference
low-power
reconfigurable approximate multipliers
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
5.2
论文数:
9.8K
被引数:
2.2W
机构
引用论文
.png){kind=icon}
A Theoretical Framework for Quality Estimation and Optimization of DSP Applications Using Low-Power Approximate Adders使用低功耗近似加法器的DSP应用质量估计和优化的理论框架
PREAI
分享收藏
查看更多内容