15 citations · 34 across the 12 of their papers we have counts for
13 papers
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…
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…
COBRA: Catastrophic Bit-flip Reliability Analysis of State-Space Models
Sanjay Das, Swastik Bhattacharya, Shamik Kundu +3
State-space models (SSMs), exemplified by the Mamba architecture, have recently emerged as state-of-the-art sequence-modeling frameworks, offering linear-time scalability together…
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…
Accelerating LLM Inference with Flexible N:M Sparsity via A Fully Digital Compute-in-Memory Accelerator
Akshat Ramachandran, Souvik Kundu, Arnab Raha +3
Large language model (LLM) pruning with fixed N:M structured sparsity significantly limits the expressivity of the sparse model, yielding sub-optimal performance. In contrast, supp…
XAMBA: Enabling Efficient State Space Models on Resource-Constrained Neural Processing Units
Arghadip Das, Arnab Raha, Shamik Kundu +3
State-Space Models (SSMs) have emerged as efficient alternatives to transformers for sequential data tasks, offering linear or near-linear scalability with sequence length, making…
Enhancing Large Language Models for Hardware Verification: A Novel SystemVerilog Assertion Dataset
Anand Menon, Samit S Miftah, Shamik Kundu +7
Hardware verification is crucial in modern SoC design, consuming around 70% of development time. SystemVerilog assertions ensure correct functionality. However, existing industrial…
GraNNite: Enabling High-Performance Execution of Graph Neural Networks on Resource-Constrained Neural Processing Units
Arghadip Das, Shamik Kundu, Arnab Raha +3
Graph Neural Networks (GNNs) are vital for learning from graph-structured data, enabling applications in network analysis, recommendation systems, and speech analytics. Deploying t…
GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs
Sanjay Das, Swastik Bhattacharya, Souvik Kundu +4
Large Language Models (LLMs) have revolutionized natural language processing (NLP), excelling in tasks like text generation and summarization. However, their increasing adoption in…
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…
High-level Modeling of Manufacturing Faults in Deep Neural Network Accelerators
Shamik Kundu, Ahmet Soyyiğit, Khaza Anuarul Hoque +1
The advent of data-driven real-time applications requires the implementation of Deep Neural Networks (DNNs) on Machine Learning accelerators. Google's Tensor Processing Unit (TPU)…
Defending Hardware-based Malware Detectors against Adversarial Attacks
Abraham Peedikayil Kuruvila, Shamik Kundu, Kanad Basu
In the era of Internet of Things (IoT), Malware has been proliferating exponentially over the past decade. Traditional anti-virus software are ineffective against modern complex Ma…