AlphaBlend: Hardware-Algorithm Co-design with Mixed-Alphabet Set Multipliers for DNN Workloads

We introduce Mixed Alphabet-Aware Training (Mixed AAT), a novel hardware-aware training method that assigns distinct low-complexity multiplier sets (alphabet sets) to individual Deep Neural Network (DNN) layers based on sensitivity to weight perturbations. This method maintains full-precision accuracy while significantly reducing memory and compute costs. Achieving <1% accuracy degradation on ResNet, VGG, and MobileNet across CIFAR-10 and ImageNet, we complement Mixed AAT with two hardware solutions: an approximate near-memory digital architecture and a modified systolic array accelerator. These co-designs - a blend of Mixed AAT and hardware solutions - offer at least 3.5X energy savings and 1.3X speedup, ideal for energy-constrained applications.