4 papers
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…
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…
SHA-CNN: Scalable Hierarchical Aware Convolutional Neural Network for Edge AI
Narendra Singh Dhakad, Yuvnish Malhotra, Santosh Kumar Vishvakarma +1
This paper introduces a Scalable Hierarchical Aware Convolutional Neural Network (SHA-CNN) model architecture for Edge AI applications. The proposed hierarchical CNN model is metic…