CONV-SRAM: An Energy-Efficient SRAM With In-Memory Dot-Product Computation for Low-Power Convolutional Neural Networks
This paper presents an energy-efficient static random access memory (SRAM) with embedded dot-product computation capability, for binary-weight convolutional neural networks. A 10T bit-cell-based SRAM array is used to store the 1-b filter weights. The array implements dot-product as a weighted averag...
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| Format: | Article |
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Institute of Electrical and Electronics Engineers (IEEE)
2019
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| Online Access: | https://hdl.handle.net/1721.1/122468 |