Name
Abstract | ||
|---|---|---|
Parallel processors and GP-GPUs have been routinely used in the past to perform the computations of convolutional neural networks (CNNs). However, their large power consumption has pushed researchers towards application-specific integrated circuits and on-chip accelerators implement neural networks. Nevertheless, within the Internet of Things (IoT) scenario, even these accelerators fail to meet the power and latency constraints. To address this issue, binary-weight networks were introduced, where weights are constrained to -1 and 1. Therefore, these networks facilitate hardware implementation of neural networks by replacing multiply-and-accumulate units with simple accumulators, as well as reducing the weight storage. In this paper, we introduce a convolutional accelerator for binary-weight neural networks. The proposed architecture only consumes 128 mW at a frequency of 200 MHz and occupies 1.2 mm
<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>
when synthesized in TSMC 65 nm CMOS technology. Moreover, it achieves a high area-efficiency of 176 Gops/MGC and performance efficiency of 89%, outperforming the state-of-the-art architecture for binary-weight networks by 1.8× and 3.2×, respectively. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ISCAS.2018.8350945 | 2018 IEEE International Symposium on Circuits and Systems (ISCAS) |
Keywords | Field | DocType |
parallel processors,CMOS technology,TSMC,hardware implementation,IoT,Internet of Things,power consumption,GP-GPUs,on-chip accelerators,application-specific integrated circuits,convolutional neural networks,binary-weight neural networks,convolutional accelerator | Computer architecture,Convolution,Latency (engineering),Convolutional neural network,Computer science,CMOS,Electronic engineering,Artificial neural network,Integrated circuit,Binary number,Computation | Conference |
ISSN | ISBN | Citations |
0271-4302 | 978-1-5386-4882-7 | 3 |
PageRank | References | Authors |
0.43 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Arash Ardakani | 1 | 33 | 8.42 |
| Carlo Condo | 2 | 132 | 21.40 |
| Warren J. Gross | 3 | 1106 | 113.38 |