Abstract
For successful deployment of deep neural networks (DNNs) on resource-constrained devices, retraining-based quantization has been widely adopted to reduce the number of DRAM accesses. By properly setting training parameters, such as batch size and learning rate, bit widths of both weights and activations can be uniformly quantized down to 4 bit while maintaining full precision accuracy. In this article, we present a retraining-based mixed-precision quantization approach and its customized DNN accelerator to achieve high energy efficiency. In the proposed quantization, in the middle of retraining, an additional bit (extra quantization level) is assigned to the weights that have shown frequent switching between two contiguous quantization levels since it means that both quantization levels cannot help to reduce quantization loss. We also mitigate the gradient noise that occurs in the retraining process by taking a lower learning rate near the quantization threshold. For the proposed novel mixed-precision quantized network (MPQ-network), we have implemented a customized accelerator using a 65-nm CMOS process. In the accelerator, the proposed processing elements (PEs) can be dynamically reconfigured to process variable bit widths from 2 to 4 bit for both weights and activations. The numerical results show that the proposed quantization can achieve 1.37 {\times } better compression ratio for VGG-9 using CIFAR-10 data set compared with a uniform 4-bit (both weights and activations) model without loss of classification accuracy. The proposed accelerator also shows 1.29\times of energy savings for VGG-9 using the CIFAR-10 data set over the state-of-the-art accelerator.
Original language | English |
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Article number | 9154572 |
Pages (from-to) | 2925-2938 |
Number of pages | 14 |
Journal | IEEE Transactions on Neural Networks and Learning Systems |
Volume | 32 |
Issue number | 7 |
DOIs | |
Publication status | Published - 2021 Jul |
Bibliographical note
Publisher Copyright:© 2012 IEEE.
Keywords
- Deep neural network (DNN) accelerator
- energy-efficient accelerator
- model compression
- quantization
ASJC Scopus subject areas
- Software
- Computer Science Applications
- Computer Networks and Communications
- Artificial Intelligence