3 citations · 3 across the 19 of their papers we have counts for
9 papers · 1 filter
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…
MOSAIC: 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. Although dense matrix multiplication underlies today's CNNs and transformers, emerging architectures (state-space models, long conv…
BIDENT: Heterogeneous Operator-level Mapping for Efficient Edge Inference
Hoseok Kim, Arghadip Das, Soumendu Ghosh +2
Modern edge System-on-Chips (SoCs) integrate heterogeneous processing units (PUs) such as CPUs, GPUs, and NPUs, yet current inference stacks map entire models to a single PU, leavi…
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…
TYTAN: Taylor-series based Non-Linear Activation Engine for Deep Learning Accelerators
Soham Pramanik, Vimal William, Arnab Raha +3
The rapid advancement in AI architectures and the proliferation of AI-enabled systems have intensified the need for domain-specific architectures that enhance both the acceleration…
SafeCiM: Investigating Resilience of Hybrid Floating-Point Compute-in-Memory Deep Learning Accelerators
Swastik Bhattacharya, Sanjay Das, Anand Menon +3
Deep Neural Networks (DNNs) continue to grow in complexity with Large Language Models (LLMs) incorporating vast numbers of parameters. Handling these parameters efficiently in trad…