most citedBare-Metal RISC-V + NVDLA SoC for Efficient Deep Learning Inference

2 citations · 2 across the 2 of their papers we have counts for

collaborators

3 papers

cs.AR2026

AdaptViT: Runtime-Adaptive Vision Transformer Deployment on Custom RISC-V

Vishnu PS, Ajay Kumar M, Yike Li +2

Deploying Vision Transformers (ViTs) on low-power edge devices is challenging due to high computational demands. Conventional pruning frameworks require a separate compiled binary…

cs.AR2025★ 2 cited

Bare-Metal RISC-V + NVDLA SoC for Efficient Deep Learning Inference

Vineet Kumar, Ajay Kumar M, Yike Li +2

This paper presents a novel System-on-Chip (SoC) architecture for accelerating complex deep learning models for edge computing applications through a combination of hardware and so…

cs.AR2025

Tensor Manipulation Unit (TMU): Reconfigurable, Near-Memory Tensor Manipulation for High-Throughput AI SoC

Weiyu Zhou, Zheng Wang, Chao Chen +4

While recent advances in AI SoC design have focused heavily on accelerating tensor computation, the equally critical task of tensor manipulation, centered on high,volume data movem…