SALSA: systematic logic synthesis of approximate circuits. - Swagath Venkataramani; Amit Sabne; Vivek Joy Kozhikkottu; Kaushik Roy 001; Anand Raghunathan (DAC, 2012)

Approximate computing: An integrated hardware approach. - Vinay K. Chippa; Swagath Venkataramani; Srimat T. Chakradhar; Kaushik Roy 001; Anand Raghunathan (ACSSC, 2013)

Relax-and-retime: a methodology for energy-efficient recovery based design. - Shankar Ganesh Ramasubramanian; Swagath Venkataramani; Adithya Parandhaman; Anand Raghunathan (DAC, 2013)

Substitute-and-simplify: a unified design paradigm for approximate and quality configurable circuits. - Swagath Venkataramani; Kaushik Roy 001; Anand Raghunathan (DATE, 2013)

Quality programmable vector processors for approximate computing. - Swagath Venkataramani; Vinay K. Chippa; Srimat T. Chakradhar; Kaushik Roy 001; Anand Raghunathan (MICRO, 2013)

ASLAN: Synthesis of approximate sequential circuits. - Ashish Ranjan 001; Arnab Raha; Swagath Venkataramani; Kaushik Roy 001; Anand Raghunathan (DATE, 2014)

Approximate computing for efficient information processing. - Swagath Venkataramani; Srimat T. Chakradhar; Kaushik Roy 001; Anand Raghunathan (ESTIMedia, 2014)

STAG: Spintronic-Tape Architecture for GPGPU cache hierarchies. - Rangharajan Venkatesan; Shankar Ganesh Ramasubramanian; Swagath Venkataramani; Kaushik Roy 001; Anand Raghunathan (ISCA, 2014)

StoRM: a stochastic recognition and mining processor. - Vinay K. Chippa; Swagath Venkataramani; Kaushik Roy 001; Anand Raghunathan (ISLPED, 2014)

Variation tolerant design of a vector processor for recognition, mining and synthesis. - Vivek Joy Kozhikkottu; Swagath Venkataramani; Sujit Dey; Anand Raghunathan (ISLPED, 2014)

AxNN: energy-efficient neuromorphic systems using approximate computing. - Swagath Venkataramani; Ashish Ranjan 001; Kaushik Roy 001; Anand Raghunathan (ISLPED, 2014)

Exploiting On-Device Image Classification for Energy Efficiency in Ambient-Aware Systems. - Mohammed Shoaib; Swagath Venkataramani; Xian-Sheng Hua 001; Jie Liu 001; Jin Li 001 (Mobile Cloud Visual Media Computing, 2015)

Approximate storage for energy efficient spintronic memories. - Ashish Ranjan 001; Swagath Venkataramani; Xuanyao Fong; Kaushik Roy 001; Anand Raghunathan (DAC, 2015)

Approximate computing and the quest for computing efficiency. - Swagath Venkataramani; Srimat T. Chakradhar; Kaushik Roy 001; Anand Raghunathan (DAC, 2015)

Scalable-effort classifiers for energy-efficient machine learning. - Swagath Venkataramani; Anand Raghunathan; Jie Liu 001; Mohammed Shoaib (DAC, 2015)

Quality configurable reduce-and-rank for energy efficient approximate computing. - Arnab Raha; Swagath Venkataramani; Vijay Raghunathan; Anand Raghunathan (DATE, 2015)

SAPPHIRE: an always-on context-aware computer vision system for portable devices. - Swagath Venkataramani; Victor Bahl; Xian-Sheng Hua 001; Jie Liu 001; Jin Li 001; Matthai Philipose; Bodhi Priyantha; Mohammed Shoaib (DATE, 2015)

Computing approximately, and efficiently. - Swagath Venkataramani; Srimat T. Chakradhar; Kaushik Roy 001; Anand Raghunathan (DATE, 2015)

Spintastic: spin-based stochastic logic for energy-efficient computing. - Rangharajan Venkatesan; Swagath Venkataramani; Xuanyao Fong; Kaushik Roy 001; Anand Raghunathan (DATE, 2015)

Object Detection using Semantic Decomposition for Energy-Efficient Neural Computing. - Priyadarshini Panda; Abhronil Sengupta; Swagath Venkataramani; Anand Raghunathan; Kaushik Roy 001 (CoRR, 2015)

Exploring Spin-Transfer-Torque Devices for Logic Applications. - Zoha Pajouhi; Swagath Venkataramani; Karthik Yogendra; Anand Raghunathan; Kaushik Roy 001 (IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2015)

Efficient embedded learning for IoT devices. - Swagath Venkataramani; Kaushik Roy 001; Anand Raghunathan (ASP-DAC, 2016)

Designing approximate circuits using clock overgating. - Younghoon Kim; Swagath Venkataramani; Kaushik Roy 001; Anand Raghunathan (DAC, 2016)

Invited - Cross-layer approximations for neuromorphic computing: from devices to circuits and systems. - Priyadarshini Panda; Abhronil Sengupta; Syed Shakib Sarwar; Gopalakrishnan Srinivasan; Swagath Venkataramani; Anand Raghunathan; Kaushik Roy 001 (DAC, 2016)

Approximation through logic isolation for the design of quality configurable circuits. - Shubham Jain; Swagath Venkataramani; Anand Raghunathan (DATE, 2016)

Multiplier-less Artificial Neurons exploiting error resiliency for energy-efficient neural computing. - Syed Shakib Sarwar; Swagath Venkataramani; Anand Raghunathan; Kaushik Roy 001 (DATE, 2016)

STOCK: Stochastic Checkers for Low-overhead Approximate Error Detection. - Neel Gala; Swagath Venkataramani; Anand Raghunathan; V. Kamakoti 001 (ISLPED, 2016)

Approximate Computing. - Swagath Venkataramani; Kaushik Roy 001; Anand Raghunathan (VLSID, 2016)

EMBIRA: An Accelerator for Model-Based Iterative Reconstruction. - Junshi Liu; Swagath Venkataramani; Singanallur V. Venkatakrishnan; Yun Pan; Charles A. Bouman; Anand Raghunathan (IEEE Trans. Very Large Scale Integr. Syst., 2016)

Approximate computing: An integrated cross-layer framework - Swagath Venkataramani (2016)

POSTER: Design Space Exploration for Performance Optimization of Deep Neural Networks on Shared Memory Accelerators. - Swagath Venkataramani; Jungwook Choi; Vijayalakshmi Srinivasan; Kailash Gopalakrishnan; Leland Chang (PACT, 2017)

Accelerator Design for Deep Learning Training: Extended Abstract: Invited. - Ankur Agrawal; Chia-Yu Chen; Jungwook Choi; Kailash Gopalakrishnan; Jinwook Oh; Sunil Shukla; Viji Srinivasan; Swagath Venkataramani; Wei Zhang 022 (DAC, 2017)

STAxCache: An approximate, energy efficient STT-MRAM cache. - Ashish Ranjan 001; Swagath Venkataramani; Zoha Pajouhi; Rangharajan Venkatesan; Kaushik Roy 001; Anand Raghunathan (DATE, 2017)

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

Very Low Voltage (VLV) Design. - Ramon Bertran; Pradip Bose; David M. Brooks; Jeff Burns; Alper Buyuktosunoglu; Nandhini Chandramoorthy; Eric Cheng; Martin Cochet; Schuyler Eldridge; Daniel Friedman; Hans M. Jacobson; Rajiv V. Joshi; et al. (ICCD, 2017)

ScaleDeep: A Scalable Compute Architecture for Learning and Evaluating Deep Networks. - Swagath Venkataramani; Ashish Ranjan 001; Subarno Banerjee; Dipankar Das 002; Sasikanth Avancha; Ashok Jagannathan; Ajaya Durg; Dheemanth Nagaraj; Bharat Kaul; Pradeep Dubey; Anand Raghunathan (ISCA, 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)

DyVEDeep: Dynamic Variable Effort Deep Neural Networks. - Sanjay Ganapathy; Swagath Venkataramani; Balaraman Ravindran; Anand Raghunathan (CoRR, 2017)

Approximate Error Detection With Stochastic Checkers. - Neel Gala; Swagath Venkataramani; Anand Raghunathan; V. Kamakoti 001 (IEEE Trans. Very Large Scale Integr. Syst., 2017)

Energy-Efficient Object Detection Using Semantic Decomposition. - Priyadarshini Panda; Swagath Venkataramani; Abhronil Sengupta; Anand Raghunathan; Kaushik Roy 001 (IEEE Trans. Very Large Scale Integr. Syst., 2017)

Energy-Efficient Reduce-and-Rank Using Input-Adaptive Approximations. - Arnab Raha; Swagath Venkataramani; Vijay Raghunathan; Anand Raghunathan (IEEE Trans. Very Large Scale Integr. Syst., 2017)

Compensated-DNN: energy efficient low-precision deep neural networks by compensating quantization errors. - Shubham Jain; Swagath Venkataramani; Vijayalakshmi Srinivasan; Jungwook Choi; Pierce Chuang; Leland Chang (DAC, 2018)

Dyhard-DNN: even more DNN acceleration with dynamic hardware reconfiguration. - Mateja Putic; Swagath Venkataramani; Schuyler Eldridge; Alper Buyuktosunoglu; Pradip Bose; Mircea Stan (DAC, 2018)

Exploiting approximate computing for deep learning acceleration. - Chia-Yu Chen; Jungwook Choi; Kailash Gopalakrishnan; Viji Srinivasan; Swagath Venkataramani (DATE, 2018)

Across the Stack Opportunities for Deep Learning Acceleration. - Vijayalakshmi Srinivasan; Bruce M. Fleischer; Sunil Shukla; Matthew M. Ziegler; Joel Silberman; Jinwook Oh; Jungwook Choi; Silvia M. Mueller; Ankur Agrawal; Tina Babinsky; Nianzheng Cao; Chia-Yu Chen; et al. (ISLPED, 2018)

Taming the beast: Programming Peta-FLOP class Deep Learning Systems. - Swagath Venkataramani; Vijayalakshmi Srinivasan; Jungwook Choi; Kailash Gopalakrishnan; Leland Chang (ISLPED, 2018)

A Scalable Multi- TeraOPS Deep Learning Processor Core for AI Trainina and Inference. - Bruce M. Fleischer; Sunil Shukla; Matthew M. Ziegler; Joel Silberman; Jinwook Oh; Vijayalakshmi Srinivasan; Jungwook Choi; Silvia M. Mueller; Ankur Agrawal; Tina Babinsky; Nianzheng Cao; Chia-Yu Chen; et al. (VLSI Circuits, 2018)

PACT: Parameterized Clipping Activation for Quantized Neural Networks. - Jungwook Choi; Zhuo Wang; Swagath Venkataramani; Pierce I-Jen Chuang; Vijayalakshmi Srinivasan; Kailash Gopalakrishnan (CoRR, 2018)

Bridging the Accuracy Gap for 2-bit Quantized Neural Networks (QNN). - Jungwook Choi; Pierce I-Jen Chuang; Zhuo Wang; Swagath Venkataramani; Vijayalakshmi Srinivasan; Kailash Gopalakrishnan (CoRR, 2018)

Energy-Efficient Neural Computing with Approximate Multipliers. - Syed Shakib Sarwar; Swagath Venkataramani; Aayush Ankit; Anand Raghunathan; Kaushik Roy 001 (ACM J. Emerg. Technol. Comput. Syst., 2018)

Approximate Computing Techniques for Deep Neural Networks. - Jungwook Choi; Swagath Venkataramani (Approximate Circuits, 2019)

Automatic Synthesis Techniques for Approximate Circuits. - Ashish Ranjan 001; Swagath Venkataramani; Shubham Jain; Younghoon Kim; Shankar Ganesh Ramasubramanian; Arnab Raha; Kaushik Roy 001; Anand Raghunathan (Approximate Circuits, 2019)

A Compiler for Deep Neural Network Accelerators to Generate Optimized Code for a Wide Range of Data Parameters from a Hand-crafted Computation Kernel. - Eri Ogawa; Kazuaki Ishizaki; Hiroshi Inoue; Swagath Venkataramani; Jungwook Choi; Wei Wang 333; Vijayalakshmi Srinivasan; Moriyoshi Ohara; Kailash Gopalakrishnan (COOL CHIPS, 2019)

BiScaled-DNN: Quantizing Long-tailed Datastructures with Two Scale Factors for Deep Neural Networks. - Shubham Jain; Swagath Venkataramani; Vijayalakshmi Srinivasan; Jungwook Choi; Kailash Gopalakrishnan; Leland Chang (DAC, 2019)

Data Subsetting: A Data-Centric Approach to Approximate Computing. - Younghoon Kim; Swagath Venkataramani; Nitin Chandrachoodan; Anand Raghunathan (DATE, 2019)

Memory and Interconnect Optimizations for Peta-Scale Deep Learning Systems. - Swagath Venkataramani; Vijayalakshmi Srinivasan; Jungwook Choi; Philip Heidelberger; Leland Chang; Kailash Gopalakrishnan (HiPC, 2019)

Workload-aware Automatic Parallelization for Multi-GPU DNN Training. - Sungho Shin; Youngmin Jo; Jungwook Choi; Swagath Venkataramani; Vijayalakshmi Srinivasan; Wonyong Sung (ICASSP, 2019)

Performance-driven Programming of Multi-TFLOP Deep Learning Accelerators. - Swagath Venkataramani; Jungwook Choi; Vijayalakshmi Srinivasan; Kailash Gopalakrishnan; Leland Chang (IISWC, 2019)

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

Accurate and Efficient 2-bit Quantized Neural Networks. - Jungwook Choi; Swagath Venkataramani; Vijayalakshmi Srinivasan; Kailash Gopalakrishnan; Zhuo Wang; Pierce Chuang (SysML, 2019)

Hybrid 8-bit Floating Point (HFP8) Training and Inference for Deep Neural Networks. - Xiao Sun 013; Jungwook Choi; Chia-Yu Chen; Naigang Wang; Swagath Venkataramani; Vijayalakshmi Srinivasan; Xiaodong Cui; Wei Zhang 022; Kailash Gopalakrishnan (NeurIPS, 2019)

DeepTools: Compiler and Execution Runtime Extensions for RaPiD AI Accelerator. - Swagath Venkataramani; Jungwook Choi; Vijayalakshmi Srinivasan; Wei Wang 333; Jintao Zhang; Marcel Schaal; Mauricio J. Serrano; Kazuaki Ishizaki; Hiroshi Inoue; Eri Ogawa; Moriyoshi Ohara; Leland Chang; et al. (IEEE Micro, 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)

ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training. - Chia-Yu Chen; Jiamin Ni; Songtao Lu; Xiaodong Cui; Pin-Yu Chen; Xiao Sun 013; Naigang Wang; Swagath Venkataramani; Vijayalakshmi Srinivasan; Wei Zhang 022; Kailash Gopalakrishnan (NeurIPS, 2020)

Ultra-Low Precision 4-bit Training of Deep Neural Networks. - Xiao Sun 013; Naigang Wang; Chia-Yu Chen; Jiamin Ni; Ankur Agrawal; Xiaodong Cui; Swagath Venkataramani; Kaoutar El Maghraoui; Vijayalakshmi Srinivasan; Kailash Gopalakrishnan (NeurIPS, 2020)

A 3.0 TFLOPS 0.62V Scalable Processor Core for High Compute Utilization AI Training and Inference. - Jinwook Oh; Sae Kyu Lee; Mingu Kang; Matthew M. Ziegler; Joel Silberman; Ankur Agrawal; Swagath Venkataramani; Bruce M. Fleischer; Michael Guillorn; Jungwook Choi; Wei Wang 333; Silvia M. Mueller; et al. (VLSI Circuits, 2020)

Efficient AI System Design With Cross-Layer Approximate Computing. - Swagath Venkataramani; Xiao Sun 013; Naigang Wang; Chia-Yu Chen; Jungwook Choi; Mingu Kang; Ankur Agarwal; Jinwook Oh; Shubham Jain; Tina Babinsky; Nianzheng Cao; Thomas W. Fox; et al. (Proc. IEEE, 2020)

Logic Synthesis of Approximate Circuits. - Swagath Venkataramani; Vivek Joy Kozhikkottu; Amit Sabne; Kaushik Roy 001; Anand Raghunathan (IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2020)

DyVEDeep: Dynamic Variable Effort Deep Neural Networks. - Sanjay Ganapathy; Swagath Venkataramani; Giridhur Sriraman; Balaraman Ravindran; Anand Raghunathan (ACM Trans. Embed. Comput. Syst., 2020)

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

4-Bit Quantization of LSTM-Based Speech Recognition Models. - Andrea Fasoli; Chia-Yu Chen; Mauricio J. Serrano; Xiao Sun 013; Naigang Wang; Swagath Venkataramani; George Saon; Xiaodong Cui; Brian Kingsbury; Wei Zhang 022; Zoltán Tüske; Kailash Gopalakrishnan (Interspeech, 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)

Efficient Management of Scratch-Pad Memories in Deep Learning Accelerators. - Subhankar Pal; Swagath Venkataramani; Viji Srinivasan; Kailash Gopalakrishnan (ISPASS, 2021)

A 7nm 4-Core AI Chip with 25.6TFLOPS Hybrid FP8 Training, 102.4TOPS INT4 Inference and Workload-Aware Throttling. - Ankur Agrawal; Sae Kyu Lee; Joel Silberman; Matthew M. Ziegler; Mingu Kang; Swagath Venkataramani; Nianzheng Cao; Bruce M. Fleischer; Michael Guillorn; Matt Cohen; Silvia M. Mueller; Jinwook Oh; et al. (ISSCC, 2021)

Approximate Computing and the Efficient Machine Learning Expedition. - Jörg Henkel; Hai Li 001; Anand Raghunathan; Mehdi B. Tahoori; Swagath Venkataramani; Xiaoxuan Yang 001; Georgios Zervakis 001 (ICCAD, 2022)

Accelerating Inference and Language Model Fusion of Recurrent Neural Network Transducers via End-to-End 4-bit Quantization. - Andrea Fasoli; Chia-Yu Chen; Mauricio J. Serrano; Swagath Venkataramani; George Saon; Xiaodong Cui; Brian Kingsbury; Kailash Gopalakrishnan (INTERSPEECH, 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)

A 7-nm Four-Core Mixed-Precision AI Chip With 26.2-TFLOPS Hybrid-FP8 Training, 104.9-TOPS INT4 Inference, and Workload-Aware Throttling. - Sae Kyu Lee; Ankur Agrawal; Joel Silberman; Matthew M. Ziegler; Mingu Kang; Swagath Venkataramani; Nianzheng Cao; Bruce M. Fleischer; Michael Guillorn; Matthew Cohen; Silvia M. Mueller; Jinwook Oh; et al. (IEEE J. Solid State Circuits, 2022)

OnSRAM: Efficient Inter-Node On-Chip Scratchpad Management in Deep Learning Accelerators. - Subhankar Pal; Swagath Venkataramani; Viji Srinivasan; Kailash Gopalakrishnan (ACM Trans. Embed. Comput. Syst., 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)

Enhance DNN Adversarial Robustness and Efficiency via Injecting Noise to Non-Essential Neurons. - Zhenyu Liu; Garrett Gagnon; Swagath Venkataramani; Liu Liu (CoRR, 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)