Name
Abstract | ||
|---|---|---|
Deep neural network (DNN) has recently attracted tremendous attention in various fields. But the computing operation requirement and the memory bottleneck limit the energy efficiency of hardware implementations. Binary quantization is proposed to relieve the pressure of hardware design. And the Computing-In-Memory (CIM) is regarded as a promising method to resolve the memory wall challenge. However, the binary computing paradigm is mismatched with the CIM scheme, which incurs complex circuits and peripheral to realize binary operation in previous works. To overcome previous issues, this work presents Binary Representation Computation-In-Memory (BR-CIM) with several key features. (1) A lightweight computation unit is realized within the 6T SRAM array to accelerate binary computing and enlarge signal margin; (2) The reconfigurable computing scheme and mapping method support extendable bit precision to satisfy the accuracy requirement of various applications; (3) Simultaneous computing and weight loading is supported by column circuitry, which shortens the data loading latency; Several experiments are conducted to estimate algorithm accuracy, the computing latency, and power consumption. The energy efficiency reaches up to 1280 TOPs/W for binary representation. And the algorithm accuracy achieves 97.82%/76.4% on MNIST/CIFAR-100 dataset. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TCSI.2022.3185135 | IEEE Transactions on Circuits and Systems I: Regular Papers |
Keywords | DocType | Volume |
Computation-in-memory,artificial intelligence,deep neural network,binary neural network,sram,reconfigurable design | Journal | 69 |
Issue | ISSN | Citations |
10 | 1549-8328 | 0 |
PageRank | References | Authors |
0.34 | 9 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhiheng Yue | 1 | 1 | 1.03 |
| Yabing Wang | 2 | 0 | 0.34 |
| Yubin Qin | 3 | 1 | 2.04 |
| leibo liu | 4 | 816 | 116.95 |
| Shaojun Wei | 5 | 555 | 102.32 |
| shouyi yin | 6 | 579 | 99.95 |