BR-CIM: An Efficient Binary Representation Computation-In-Memory Design

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 Comp...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems. I, Regular papers Regular papers, 2022-10, Vol.69 (10), p.3940-3953
Main Authors: Yue, Zhiheng, Wang, Yabing, Qin, Yubin, Liu, Leibo, Wei, Shaojun, Yin, Shouyi
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Arrays
artificial intelligence
Artificial neural networks
binary neural network
Circuits
Common Information Model (computing)
Computation-in-memory
Computer architecture
Computer memory
deep neural network
Energy efficiency
Hardware
Microprocessors
Neural networks
Power consumption
Quantization (signal)
Random access memory
reconfigurable design
Representations
sram
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