Approximate computing for spiking neural networks. - Sanchari Sen; Swagath Venkataramani; Anand Raghunathan (DATE, 2017)

A Programmable Event-driven Architecture for Evaluating Spiking Neural Networks. - Arnab Roy 003; Swagath Venkataramani; Neel Gala; Sanchari Sen; Kamakoti Veezhinathan; Anand Raghunathan (ISLPED, 2017)

Approximate Computing for Long Short Term Memory (LSTM) Neural Networks. - Sanchari Sen; Anand Raghunathan (IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2018)

Dynamic Spike Bundling for Energy-Efficient Spiking Neural Networks. - Sarada Krithivasan; Sanchari Sen; Swagath Venkataramani; Anand Raghunathan (ISLPED, 2019)

SparCE: Sparsity Aware General-Purpose Core Extensions to Accelerate Deep Neural Networks. - Sanchari Sen; Shubham Jain; Swagath Venkataramani; Anand Raghunathan (IEEE Trans. Computers, 2019)

Sparsity-Aware Caches to Accelerate Deep Neural Networks. - Vinod Ganesan; Sanchari Sen; Pratyush Kumar; Neel Gala; Kamakoti Veezhinathan; Anand Raghunathan (DATE, 2020)

EMPIR: Ensembles of Mixed Precision Deep Networks for Increased Robustness Against Adversarial Attacks. - Sanchari Sen; Balaraman Ravindran; Anand Raghunathan (ICLR, 2020)

A Case for Generalizable DNN Cost Models for Mobile Devices. - Vinod Ganesan; Surya Selvam; Sanchari Sen; Pratyush Kumar; Anand Raghunathan (IISWC, 2020)

Adversarial Sparsity Attacks on Deep Neural Networks. - Sarada Krithivasan; Sanchari Sen; Anand Raghunathan (CoRR, 2020)

Optimizing Transformers with Approximate Computing for Faster, Smaller and more Accurate NLP Models. - Amrit Nagarajan; Sanchari Sen; Jacob R. Stevens; Anand Raghunathan (CoRR, 2020)

Sparsity Turns Adversarial: Energy and Latency Attacks on Deep Neural Networks. - Sarada Krithivasan; Sanchari Sen; Anand Raghunathan (IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2020)

Value Similarity Extensions for Approximate Computing in General-Purpose Processors. - Younghoon Kim; Swagath Venkataramani; Sanchari Sen; Anand Raghunathan (DATE, 2021)

RaPiD: AI Accelerator for Ultra-low Precision Training and Inference. - Swagath Venkataramani; Vijayalakshmi Srinivasan; Wei Wang 333; Sanchari Sen; Jintao Zhang; Ankur Agrawal; Monodeep Kar; Shubham Jain; Alberto Mannari; Hoang Tran; Yulong Li; Eri Ogawa; et al. (ISCA, 2021)

Efficacy of Pruning in Ultra-Low Precision DNNs. - Sanchari Sen; Swagath Venkataramani; Anand Raghunathan (ISLPED, 2021)

Quantifying the Brain Predictivity of Artificial Neural Networks With Nonlinear Response Mapping. - Aditi Anand; Sanchari Sen; Kaushik Roy 001 (Frontiers Comput. Neurosci., 2021)

Efficiency attacks on spiking neural networks. - Sarada Krithivasan; Sanchari Sen; Nitin Rathi; Kaushik Roy 001; Anand Raghunathan (DAC, 2022)

Seprox: Sequence-Based Approximations for Compressing Ultra-Low Precision Deep Neural Networks. - Aradhana Mohan Parvathy; Sarada Krithivasan; Sanchari Sen; Anand Raghunathan (ICCAD, 2022)

AxFormer: Accuracy-driven Approximation of Transformers for Faster, Smaller and more Accurate NLP Models. - Amrit Nagarajan; Sanchari Sen; Jacob R. Stevens; Anand Raghunathan (IJCNN, 2022)

Layerwise Disaggregated Evaluation of Spiking Neural Networks. - Abinand Nallathambi; Sanchari Sen; Anand Raghunathan; Nitin Chandrachoodan (ISLPED, 2022)

Deep Compression of Pre-trained Transformer Models. - Naigang Wang; Chi-Chun (Charlie) Liu; Swagath Venkataramani; Sanchari Sen; Chia-Yu Chen; Kaoutar El Maghraoui; Vijayalakshmi Srinivasan; Leland Chang (NeurIPS, 2022)

14.1 A Software-Assisted Peak Current Regulation Scheme to Improve Power-Limited Inference Performance in a 5nm AI SoC. - Monodeep Kar; Joel Silberman; Swagath Venkataramani; Viji Srinivasan; Bruce M. Fleischer; Joshua Rubin; JohnDavid Lancaster; Sae Kyu Lee; Matthew Cohen; Matthew M. Ziegler; Nianzheng Cao; Sandra Woodward; et al. (ISSCC, 2024)

SmartQuant: CXL-Based AI Model Store in Support of Runtime Configurable Weight Quantization. - Rui Xie 006; Asad Ul Haq; Linsen Ma; Krystal Sun; Sanchari Sen; Swagath Venkataramani; Liu Liu 017; Tong Zhang 002 (IEEE Comput. Archit. Lett., 2024)

MixTrain: accelerating DNN training via input mixing. - Sarada Krithivasan; Sanchari Sen; Swagath Venkataramani; Anand Raghunathan (Frontiers Artif. Intell., 2024)

DNNDaSher: A Compiler Framework for Dataflow Compatible End-to-End Acceleration on IBM AIU. - Sanchari Sen; Shubham Jain; Sarada Krithivasan; Swagath Venkataramani; Vijayalakshmi Srinivasan (IEEE Micro, 2024)

Power-Limited Inference Performance Optimization Using a Software-Assisted Peak Current Regulation Scheme in a 5-nm AI SoC. - Monodeep Kar; Joel Silberman; Swagath Venkataramani; Viji Srinivasan; Bruce M. Fleischer; Joshua Rubin; JohnDavid Lancaster; Sae Kyu Lee; Matthew Cohen; Matthew M. Ziegler; Nianzheng Cao; Sandra Woodward; et al. (IEEE J. Solid State Circuits, 2025)

Breaking the HBM Bit Cost Barrier: Domain-Specific ECC for AI Inference Infrastructure. - Rui Xie 006; Asad Ul Haq; Yunhua Fang; Linsen Ma; Sanchari Sen; Swagath Venkataramani; Liu Liu 017; Tong Zhang 002 (CoRR, 2025)