REQAP: Resilient Weight Packing and Quantization for Edge DNN Acceleration
Efficient deployment of Deep Neural Networks (DNNs) on edge accelerators requires aggressive model compression while maintaining reliability in fault-prone hardware environments. This paper presents a reliability-aware quantized weight packing methodology for systolic-array-based DNN accelerators. A sensitivity-driven mixed-precision quantization framework assigns layer-wise bit-widths according to accuracy impact while enforcing symmetric precision between weights and activations. A deterministic register-level packing strategy consolidates multiple heterogeneous operand pairs into fixed-width register words, enabling SIMD-within-a-register (SWAR) style parallel execution that reduces bo...
| Authors | Mahdi Taheri, Samira Nazari, Mubassher Ansari et al. |
|---|---|
| Primary category | cs.AR |
| Published | 20 July 2026 |
| Last updated at source | 20 July 2026 |
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