Skip to content
Log in
Sponsored AI AM CLAUDE, A Beginner's Guide: Learn the basics of working with AI AI AM CLAUDE, A Beginner's Guide: Learn the basics of working with AI

Author

Arnab Raha

h-index 25 View on Semantic Scholar

We have 4 of 141 papers

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

#edge computing Open access Oct 2026

StEdge: A Low-Power Real-Time Hardware Accelerator for Edge Detection Using Stochastic Computing

Edge detection is a fundamental operation in real-time image and video processing systems. However, conventional gradient-based hardware implementations incur significant power, area, and computational overheads. This work presents StEdge, a low-power, configurable hardware accelerator for real-time edge detection base...

Priyajit Ghosh, Rajarshi Mukherjee, Auro Anand Saha et al. · 0 citations
#generative ai Open access Sep 2026

NEXUS: Speeding Up the Next Wave of AI Workloads, GNN and SSM Optimization on NPUs

NEXUS is presented, a unified framework that establishes a systematic methodology for accelerating emerging AI workloads on commercial-off-the-shelf (COTS) Neural Processing Units (NPUs) and introduces a general systematic three-step approach.

Arghadip Das, Arnab Raha, Shamik Kundu et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Lightweight Vision Transformer Compression for On-Device Plant Disease Detection in Resource-Constrained Agricultural Field Conditions

This work proposes a unified Vision Transformer compression framework that combines Hessian-Balanced Adaptive Block Pruning (H-BAC), guided by second-order sensitivity estimation, with quantization and attention-based knowledge distillation, and systematically identifies the most effective configuration within each com...

M. Kumar, Bhavika Gondi, Desaisetty Venkata Satya Sai Swapnith et al. · 0 citations
#artificial intelligence Preprint Aug 2026

HBQ: Hierarchical Scaling Block Quantization with Hardware-Efficiency-Aware Design for Accurate LLM Inference

Hierarchical Block Quantization (HBQ) uses large blocks to maximize efficiency and introduces low-overhead significand (SIG) scaling for second-level quantization and compensates for large-block errors more effectively than prior PoT and INT schemes.

Chun-Ting Chen, Dongguang Han, Han-Gyeol Mun et al. · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.