70 citations · 99 across the 22 of their papers we have counts for
7 papers · 1 filter
Lightweight Vision Transformer Compression for On-Device Plant Disease Detection in Resource-Constrained Agricultural Field Conditions
Mahadev Sunil Kumar, Bhavika Gondi, Desaisetty Venkata Satya Sai Swapnith +6
Chilli (Capsicum annuum) is one of India's most economically significant crops, yet its productivity is persistently threatened by diseases that are difficult to identify without e…
HBQ: Hierarchical Scaling Block Quantization with Hardware-Efficiency-Aware Design for Accurate LLM Inference
Chun-Ting Chen, Dongmin Han, Hangyeol Mun +6
Block Quantization (BQ) enables efficient LLM inference by quantizing both weights and activations, but its design space remains underexplored. Through hardware-accuracy design spa…
ReRAM-aware Model Finetuning addressing I-V Non-linearity and Retention Errors
Ching-Yi Lin, Shamik Kundu, Arnab Raha +1
Traditional CPU, GPU, and NPU architectures are increasingly limited by the von Neumann bottleneck. While In-Memory Computing (IMC) using ReRAM crossbar arrays offers a high-densit…
KATANA: A Fast, Low-Power Mapping of Kalman Filters onto Edge NPUs for Real-Time Tracking
Bodhisatwa Kundu, Anish Rooj, Sumit Saha +4
State estimation is the closed-loop core of every real-time tracking system, from radar surveillance and counter-UAV defense to autonomous driving and robotics. These deployments r…
TESSERA: A Workload-Driven Simulation and Design-Space Exploration Framework for Heterogeneous NPUs
Arghadip Das, Hoseok Kim, Soomin Lee +3
AI model architectures are diversifying rapidly. While CNNs and transformers rely on dense matrix multiplication, emerging architectures (state-space models, fast Fourier transform…
BIDENT: Heterogeneous Operator-level Mapping for Efficient Edge Inference
Hoseok Kim, Arghadip Das, Soumendu Ghosh +2
Modern edge System-on-Chips (SoCs) integrate increasingly heterogeneous processing units (PUs) such as CPUs, GPUs, and NPUs, yet current inference stacks still map entire models to…
SPARQLe: Sub-Precision Activation Representation for Quantized LLM Inference
Aradhana Mohan Parvathy, Soumendu Kumar Ghosh, Shamik Kundu +4
The rapid growth in sizes of Large language models (LLMs) results in high compute and memory costs during inference. Quantization has been a significant pathway to addressing this…