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Exploiting resiliency for Kernel-wise CNN approximation enabled by adaptive hardware Design
- Source :
- IEEE International Symposium on Circuits and Systems (ISCAS), 2021 IEEE International Symposium on Circuits and Systems (ISCAS), ISCAS
- Publication Year :
- 2021
-
Abstract
- Efficient low-power accelerators for Convolutional Neural Networks (CNNs) largely benefit from quantization and approximation, which are typically applied layer-wise for efficient hardware implementation. In this work, we present a novel strategy for efficient combination of these concepts at a deeper level, which is at each channel or kernel. We first apply layer-wise, low bit-width, linear quantization and truncation-based approximate multipliers to the CNN computation. Then, based on a state-of-the-art resiliency analysis, we are able to apply a kernel-wise approximation and quantization scheme with negligible accuracy losses, without further retraining. Our proposed strategy is implemented in a specialized framework for fast design space exploration. This optimization leads to a boost in estimated power savings of up to 34% in residual CNN architectures for image classification, compared to the base quantized architecture.
Details
- ISBN :
- 978-1-72819-201-7
- ISBNs :
- 9781728192017
- Database :
- OpenAIRE
- Journal :
- IEEE International Symposium on Circuits and Systems (ISCAS)
- Accession number :
- edsair.doi.dedup.....3d8f49c00d059b65763e26c900f659b2
- Full Text :
- https://doi.org/10.1109/iscas51556.2021.9401517