ApproxEye: Enabling approximate computation reuse for microrobotic computer vision

He, Xin; Yan, Guihai; Sun, Fan; Li, Xiaowei

doi:10.1109/aspdac.2017.7858356

Cited by 5 publications

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other approaches such as ApproxEye [29], Deep-Mon [30], CBinfer [31], and DeepCache [32] attempted to use the same concept to enhance the applications' performance. ApproxEye is a framework for partial reuse of approximate computations to improve computer vision of micro-robots.…”

Section: Related Workmentioning

confidence: 99%

A Network-based Compute Reuse Architecture for IoT Applications

Nour,

Cherkaoui

2021

Preprint

View full text Add to dashboard Cite

Section: Related Workmentioning

confidence: 99%

A Network-based Compute Reuse Architecture for IoT Applications

Nour,

Cherkaoui

2021

Preprint

View full text Add to dashboard Cite

“…at the architecture, circuit, and device levels. Customized NN accelerators aim at higher energy efficiency, approximate circuits trade accuracy for energy efficiency [5,6], and emerging technologies (e.g. RRAM crossbar) perform low power NN computation in memory [7].…”

mentioning

confidence: 99%

AxTrain

Liu

et al. 2018

Proceedings of the International Symposium on Low Power Electronics and Design

Self Cite

View full text Add to dashboard Cite

The intrinsic error tolerance of neural network (NN) makes approximate computing a promising technique to improve the energy efficiency of NN inference. Conventional approximate computing focuses on balancing the efficiency-accuracy trade-off for existing pre-trained networks, which can lead to suboptimal solutions. In this paper, we propose AxTrain, a hardware-oriented training framework to facilitate approximate computing for NN inference. Specifically, AxTrain leverages the synergy between two orthogonal methods-one actively searches for a network parameters distribution with high error tolerance, and the other passively learns resilient weights by numerically incorporating the noise distributions of the approximate hardware in the forward pass during the training phase. Experimental results from various datasets with near-threshold computing and approximation multiplication strategies demonstrate AxTrain's ability to obtain resilient neural network parameters and system energy efficiency improvement. INTRODUCTIONAn Artificial Neural Network (ANN) is a biologically inspired machine learning model that has been practically demonstrated to deliver superior performance in many recognition, mining, and synthesis (RMS) applications [1]. The success of ANN can be attributed to innovations across the computing system stack: To achieve higher accuracy, deeper and more complex networks are created along with more advanced training algorithms. To speed up NN training and deployment, powerful parallel computing engines (e.g., GPUs) are designed to accelerate computationally intensive mathematical operations. Despite the improved performance, energy efficiency still remains a limiting factor when deploying advanced ANNs into edge devices with stringent power budgets.A growing body of research has been proposed to tackle energy efficiency from diverse perspectives. Algorithmically, the focus is to simplify neural network (NN) by either using more concise network models (e.g. ResNet [2] and binary neural networks [3]) or pruning and compressing existing models [4]. From the hardware perspective, efficiency-driven optimizations have been conducted Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org.

show abstract

Probability gate model based methods for approximate arithmetic circuits reliability estimation

Jiang

Wang

2021

CCF Trans. HPC

View full text Add to dashboard Cite

ApproxEye: Enabling approximate computation reuse for microrobotic computer vision

Cited by 5 publications

References 18 publications

A Network-based Compute Reuse Architecture for IoT Applications

A Network-based Compute Reuse Architecture for IoT Applications

AxTrain

Probability gate model based methods for approximate arithmetic circuits reliability estimation

Contact Info

Product

Resources

About