PoTAcc: A Pipeline for End-to-End Acceleration of Power-of-Two Quantized DNNs
Power-of-two (PoT) quantization significantly reduces the size of deep neural networks (DNNs) and replaces multiplications with bit-shift operations for inference. Prior work has shown that PoT-quantized DNNs can preserve accuracy for tasks such as image classification; however, their performance on resource-constrained edge devices remains insufficiently understood. While general-purpose edge CPUs and GPUs do not provide optimized backends for bit-shift operations, custom hardware accelerators can better exploit PoT quantization by implementing dedicated shift-based processing elements. However, deploying PoT-quantized models on such accelerators is challenging due to limited support in ...
| Authors | Rappy Saha, Jude Haris, Nicolas Bohm Agostini et al. |
|---|---|
| Primary category | cs.AR |
| Published | 7 May 2026 |
| Last updated at source | 7 May 2026 |
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