3 papers
cs.AR2026
GRAU: Generic Reconfigurable Activation Unit Design for Neural Network Hardware Accelerators
Yuhao Liu, Salim Ullah, Akash Kumar
With the continuous growth of neural network scales, low-precision quantization is widely used in edge accelerators. Classic multi-threshold activation hardware requires 2^n thresh…
cs.AR2026
BiKA: Kolmogorov-Arnold-Network-inspired Ultra Lightweight Neural Network Hardware Accelerator
Yuhao Liu, Salim Ullah, Akash Kumar
Lightweight neural network accelerators are essential for edge devices with limited resources and power constraints. While quantization and binarization can efficiently reduce hard…
cs.AR2025
AxOSyn: An Open-source Framework for Synthesizing Novel Approximate Arithmetic Operators
Siva Satyendra Sahoo, Salim Ullah, Akash Kumar
Edge AI deployments are becoming increasingly complex, necessitating energy-efficient solutions for resource-constrained embedded systems. Approximate computing, which allows for c…