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Biomimetic Materials and Structures for Sensor Applications Read chapter Read first chapter

2017 | OriginalPaper | Chapter

5. Energy Efficient RRAM Crossbar-Based Approximate Computing for Smart Cameras

Authors : Yu Wang, Boxun Li, Lixue Xia, Tianqi Tang, Huazhong Yang

Published in: Smart Sensors and Systems

Publisher: Springer International Publishing

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Abstract

Smart cameras have been applied successfully in many fields. The limited battery capacity and power efficiency restrict the local processing capacity of smart cameras. In order to shift vision processing closer to the sensors, we propose a power efficient framework for analog approximate computing with the emerging metal-oxide resistive switching random-access memory (RRAM) devices. A programmable RRAM-based approximate computing unit (RRAM-ACU) is introduced first to accelerate approximated computation, and a scalable approximate computing framework is then proposed on top of the RRAM-ACU. In order to program the RRAM-ACU efficiently, we also present a detailed configuration flow, which includes a customized approximator training scheme, an approximator-parameter-to-RRAM-state mapping algorithm, and an RRAM state tuning scheme. Simulation results on a set of diverse benchmarks demonstrate that, compared with an x86-64 CPU at 2 GHz, the RRAM-ACU is able to achieve 4.06–196.41× speedup and power efficiency of 24.59–567.98 GFLOPS/W with quality loss of 8.72 % on average. The implementation of HMAX application further demonstrates that the proposed RRAM-based approximate computing framework can achieve > 12. 8× power efficiency than the digital implementation counterparts (CPU, GPU, and FPGA).

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previous chapter Smart Sensor Microsystems: Application-Dependent Design and Integration Approaches
next chapter NVRAM-Assisted Optimization Techniques for Flash Memory Management in Embedded Sensor Nodes
Footnotes
1
A neural network will tend to overfit when many weights of the network are large [28]. Overfitting is a problem that the model learns too much, including the noise, from the training data. The trained model will have poor predictive performance on the unknown testing data which are not covered by the training set.
 
2
We use ℓ 2 regularization in the training scheme. Regularization is a technique widely used in the neural network training to limit the amplitude of network weight, avoid overfitting, and improve model generalization [28]. To be specific, for the ℓ 2 regularization, a penalty of the square of the 2-norm of network weights will be proportionally added to the loss function of the network. So the error of the network and the amplitude of weights will be balanced and optimized simultaneously in the training process [28].
 
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Metadata
Title
Energy Efficient RRAM Crossbar-Based Approximate Computing for Smart Cameras
Authors
Yu Wang
Boxun Li
Lixue Xia
Tianqi Tang
Huazhong Yang
Copyright Year
2017
Book
Smart Sensors and Systems

Print ISBN: 978-3-319-33200-0

Electronic ISBN: 978-3-319-33201-7

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-33201-7_5