3 papers
cs.AR2026
SPARX: Secure and Privacy-Aware Approximate CNN Acceleration with Edge RISC-V SoC
Sonu Kumar, Akash Sankhe, Mukul Lokhande +1
Edge-AI systems increasingly require real-time CNN inference under strict energy, performance, security, and privacy constraints. Approximate computing improves hardware efficiency…
cs.AR2025
FERMI-ML: A Flexible and Resource-Efficient Memory-In-Situ SRAM Macro for TinyML acceleration
Mukul Lokhande, Akash Sankhe, S. V. Jaya Chand +1
The growing demand for low-power and area-efficient TinyML inference on AIoT devices necessitates memory architectures that minimise data movement while sustaining high computation…
cs.AR2025
Res-DPU: Resource-shared Digital Processing-in-memory Unit for Edge-AI Workloads
Mukul Lokhande, Narendra Singh Dhakad, Seema Chouhan +2
Processing-in-memory (PIM) has emerged as the go to solution for addressing the von Neumann bottleneck in edge AI accelerators. However, state-of-the-art (SoTA) digital PIM approac…