7 papers
SPADE: A SIMD Posit-enabled compute engine for Accelerating DNN Efficiency
Sonu Kumar, Lavanya Vinnakota, Mukul Lokhande +2
The growing demand for edge-AI systems requires arithmetic units that balance numerical precision, energy efficiency, and compact hardware while supporting diverse formats. Posit a…
RAMAN: Resource-efficient ApproxiMate Posit Processing for Algorithm-Hardware Co-desigN
Mohd Faisal Khan, Mukul Lokhande, Santosh Kumar Vishvakarma
Edge-AI applications still face considerable challenges in enhancing computational efficiency in resource-constrained environments. This work presents RAMAN, a resource-efficient a…
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…
XR-NPE: High-Throughput Mixed-precision SIMD Neural Processing Engine for Extended Reality Perception Workloads
Tejas Chaudhari, Akarsh J., Tanushree Dewangan +2
This work proposes XR-NPE, a high-throughput Mixed-precision SIMD Neural Processing Engine, designed for extended reality (XR) perception workloads like visual inertial odometry (V…
POLARON: Precision-aware On-device Learning and Adaptive Runtime-cONfigurable AI acceleration
Mukul Lokhande, Santosh Kumar Vishvakarma
The increasing complexity of AI models requires flexible hardware capable of supporting diverse precision formats, particularly for energy-constrained edge platforms. This work pre…
QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine
Anushka Jha, Tanushree Dewangan, Mukul Lokhande +1
Reinforcement Learning (RL) has outperformed other counterparts in sequential decision-making and dynamic environment control. However, FPGA deployment is significantly resource-ex…