A Flexible In-Memory Computing Architecture for Heterogeneously Quantized CNNs

Flavio Ponzina,Marco Rios,Giovanni Ansaloni,Alexandre Levisse,David Atienza

2021 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)（2021）

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摘要

Inferences using Convolutional Neural Networks (CNNs) are resource and energy intensive. Therefore, their execution on highly constrained edge devices demands the careful co-optimization of algorithms and hardware. Addressing this challenge, in this paper we present a flexible In-Memory Computing (IMC) architecture and circuit, able to scale data representations to varying bitwidths at run-time, w...

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关键词

Degradation,Quantization (signal),Computer architecture,Parallel processing,Very large scale integration,Robustness,Inference algorithms

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