15 citations · 34 across the 12 of their papers we have counts for
4 papers · 1 filter
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…
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…
FlexNN: A Dataflow-aware Flexible Deep Learning Accelerator for Energy-Efficient Edge Devices
Arnab Raha, Deepak A. Mathaikutty, Soumendu K. Ghosh +1
This paper introduces FlexNN, a Flexible Neural Network accelerator, which adopts agile design principles to enable versatile dataflows, enhancing energy efficiency. Unlike convent…
Special Session: Reliability Analysis for ML/AI Hardware
Shamik Kundu, Kanad Basu, Mehdi Sadi +4
Artificial intelligence (AI) and Machine Learning (ML) are becoming pervasive in today's applications, such as autonomous vehicles, healthcare, aerospace, cybersecurity, and many c…