Input-Splitting of Large Neural Networks for Power-Efficient Accelerator with Resistive Crossbar Memory Array
- Title
- Input-Splitting of Large Neural Networks for Power-Efficient Accelerator with Resistive Crossbar Memory Array
- Authors
- KIM, Yulhwa; KIM, Hyungjun; Ahn, Daehyun; KIM, JAE JOON
- Date Issued
- 2018-07-23
- Publisher
- ACM/IEEE
- Abstract
- Resistive Crossbar memory Arrays (RCA) have been gaining interest as a promising platform to implement Convolutional Neural
Networks (CNN). One of the major challenges in RCA-based design is that the number of rows in an RCA is often smaller than
the number of input neurons in a layer. Previous works used highresolution Analog-to-Digital Converters (ADCs) to compute the
partial weighted sum in each array and merged partial sums from
multiple arrays outside the RCAs. However, such approach suffers from significant power consumption due to the need for highresolution ADCs. In this paper, we propose a methodology to more
efficiently construct a large CNN with multiple RCAs. By splitting
the input feature map and retraining the CNN with proper initialization, we demonstrate that any CNN model can be represented
with multiple arrays without using intermediate partial sums. The
experimental results show that the ADC power of the proposed
design is 32x smaller and the total chip power of the proposed
design is 3x smaller than those of the baseline design
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/97843
- Article Type
- Conference
- Citation
- International Symposium on Low Power Electronics and Design (ISLPED), 2018-07-23
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