Anand Raghunathan

ORCID

Publication Activity (10 Years)

Years Active: 1994-2024
Publications (10 Years): 119

Top Venues

CoRR

DATE

IEEE Trans. Very Large Scale Integr. Syst.

DAC

Publications

Shrihari Sridharan, Surya Selvam, Kaushik Roy, Anand Raghunathan
Ev-Edge: Efficient Execution of Event-based Vision Algorithms on Commodity Edge Platforms. CoRR (2024)
Soumendu Kumar Ghosh, Arnab Raha, Vijay Raghunathan, Anand Raghunathan
PArtNNer: Platform-Agnostic Adaptive Edge-Cloud DNN Partitioning for Minimizing End-to-End Latency. ACM Trans. Embed. Comput. Syst. 23 (1) (2024)
Abinand Nallathambi, Christin David Bose, Wilfried Haensch, Anand Raghunathan
LRMP: Layer Replication with Mixed Precision for Spatial In-memory DNN Accelerators. CoRR (2023)
Basar Kütükçü, Sabur Baidya, Anand Raghunathan, Sujit Dey
EvoSh: Evolutionary Search with Shaving to Enable Power-Latency Tradeoff in Deep Learning Computing on Embedded Systems. SOCC (2023)
Amrit Nagarajan, Anand Raghunathan
Input Compression with Positional Consistency for Efficient Training and Inference of Transformer Neural Networks. CoRR (2023)
Amrit Nagarajan, Anand Raghunathan
TokenDrop + BucketSampler: Towards Efficient Padding-free Fine-tuning of Language Models. EMNLP (Findings) (2023)
Shrihari Sridharan, Jacob R. Stevens, Kaushik Roy, Anand Raghunathan
X-Former: In-Memory Acceleration of Transformers. IEEE Trans. Very Large Scale Integr. Syst. 31 (8) (2023)
Sourjya Roy, Cheng Wang, Anand Raghunathan
Evaluation of STT-MRAM as a Scratchpad for Training in ML Accelerators. CoRR (2023)
Amrit Nagarajan, Anand Raghunathan
FASTRAIN-GNN: Fast and Accurate Self-Training for Graph Neural Networks. Trans. Mach. Learn. Res. 2023 (2023)
Shrihari Sridharan, Jacob R. Stevens, Kaushik Roy, Anand Raghunathan
X-Former: In-Memory Acceleration of Transformers. CoRR (2023)
Basar Kütükçü, Sabur Baidya, Anand Raghunathan, Sujit Dey
Contention Grading and Adaptive Model Selection for Machine Vision in Embedded Systems. ACM Trans. Embed. Comput. Syst. 21 (5) (2022)
Amrit Nagarajan, Jacob R. Stevens, Anand Raghunathan
Efficient ensembles of graph neural networks. DAC (2022)
Sarada Krithivasan, Sanchari Sen, Nitin Rathi, Kaushik Roy, Anand Raghunathan
Efficiency attacks on spiking neural networks. DAC (2022)
Gobinda Saha, Cheng Wang, Anand Raghunathan, Kaushik Roy
A cross-layer approach to cognitive computing: invited. DAC (2022)
Niharika Thakuria, Reena Elangovan, Sandeep Krishna Thirumala, Anand Raghunathan, Sumeet Kumar Gupta
STeP-CiM: Strain-enabled Ternary Precision Computation-in-Memory based on Non-Volatile 2D Piezoelectric Transistors. CoRR (2022)
Jörg Henkel, Hai Li, Anand Raghunathan, Mehdi B. Tahoori, Swagath Venkataramani, Xiaoxuan Yang, Georgios Zervakis
Approximate Computing and the Efficient Machine Learning Expedition. ICCAD (2022)
Jörg Henkel, Hai Li, Anand Raghunathan, Mehdi B. Tahoori, Swagath Venkataramani, Xiaoxuan Yang, Georgios Zervakis
Approximate Computing and the Efficient Machine Learning Expedition. CoRR (2022)
Abinand Nallathambi, Sanchari Sen, Anand Raghunathan, Nitin Chandrachoodan
Layerwise Disaggregated Evaluation of Spiking Neural Networks. ISLPED (2022)
Aradhana Mohan Parvathy, Sarada Krithivasan, Sanchari Sen, Anand Raghunathan
Seprox: Sequence-Based Approximations for Compressing Ultra-Low Precision Deep Neural Networks. ICCAD (2022)
Reena Elangovan, Ashish Ranjan, Niharika Thakuria, Sumeet Kumar Gupta, Anand Raghunathan
Energy Efficient Cache Design with Piezoelectric FETs. ISLPED (2022)
Wilfried Haensch, Anand Raghunathan, Kaushik Roy, Bhaswar Chakrabarti, Charudatta M. Phatak, Cheng Wang, Supratik Guha
A Co-design view of Compute in-Memory with Non-Volatile Elements for Neural Networks. CoRR (2022)
Reena Elangovan, Shubham Jain, Anand Raghunathan
Ax-BxP: Approximate Blocked Computation for Precision-reconfigurable Deep Neural Network Acceleration. ACM Trans. Design Autom. Electr. Syst. 27 (3) (2022)
Mustafa Fayez Ali, Sourjya Roy, Utkarsh Saxena, Tanvi Sharma, Anand Raghunathan, Kaushik Roy
Compute-in-Memory Technologies and Architectures for Deep Learning Workloads. IEEE Trans. Very Large Scale Integr. Syst. 30 (11) (2022)
Amrit Nagarajan, Sanchari Sen, Jacob R. Stevens, Anand Raghunathan
AxFormer: Accuracy-driven Approximation of Transformers for Faster, Smaller and more Accurate NLP Models. IJCNN (2022)
Jacob R. Stevens, Rangharajan Venkatesan, Steve Dai, Brucek Khailany, Anand Raghunathan
Softermax: Hardware/Software Co-Design of an Efficient Softmax for Transformers. CoRR (2021)
Jacob R. Stevens, Dipankar Das, Sasikanth Avancha, Bharat Kaul, Anand Raghunathan
GNNerator: A Hardware/Software Framework for Accelerating Graph Neural Networks. CoRR (2021)
Jacob R. Stevens, Dipankar Das, Sasikanth Avancha, Bharat Kaul, Anand Raghunathan
GNNerator: A Hardware/Software Framework for Accelerating Graph Neural Networks. DAC (2021)
Sourjya Roy, Shrihari Sridharan, Shubham Jain, Anand Raghunathan
TxSim: Modeling Training of Deep Neural Networks on Resistive Crossbar Systems. IEEE Trans. Very Large Scale Integr. Syst. 29 (4) (2021)
Malin Prematilake, Younghyun Kim, Vijay Raghunathan, Anand Raghunathan, Niraj K. Jha
HW/SW Framework for Improving the Safety of Implantable and Wearable Medical Devices. CoRR (2021)
Younghoon Kim, Swagath Venkataramani, Sanchari Sen, Anand Raghunathan
Value Similarity Extensions for Approximate Computing in General-Purpose Processors. DATE (2021)
Indranil Chakraborty, Sourjya Roy, Shrihari Sridharan, Mustafa Fayez Ali, Aayush Ankit, Shubham Jain, Anand Raghunathan
Design Tools for Resistive Crossbar based Machine Learning Accelerators. AICAS (2021)
Jacob R. Stevens, Rangharajan Venkatesan, Steve Dai, Brucek Khailany, Anand Raghunathan
Softermax: Hardware/Software Co-Design of an Efficient Softmax for Transformers. DAC (2021)
Shubham Jain, Abhronil Sengupta, Kaushik Roy, Anand Raghunathan
RxNN: A Framework for Evaluating Deep Neural Networks on Resistive Crossbars. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 40 (2) (2021)
Sanchari Sen, Swagath Venkataramani, Anand Raghunathan
Efficacy of Pruning in Ultra-Low Precision DNNs. ISLPED (2021)
Sourjya Roy, Mustafa Fayez Ali, Anand Raghunathan
PIM-DRAM: Accelerating Machine Learning Workloads Using Processing in Commodity DRAM. IEEE J. Emerg. Sel. Topics Circuits Syst. 11 (4) (2021)
Sourjya Roy, Mustafa Fayez Ali, Anand Raghunathan
PIM-DRAM: Accelerating Machine Learning Workloads using Processing in Commodity DRAM. CoRR (2021)
Basar Kütükçü, Sabur Baidya, Anand Raghunathan, Sujit Dey
Contention-aware Adaptive Model Selection for Machine Vision in Embedded Systems. AICAS (2021)
Ashish Ranjan, Arnab Raha, Vijay Raghunathan, Anand Raghunathan
Approximate Memory Compression. IEEE Trans. Very Large Scale Integr. Syst. 28 (4) (2020)
Sourjya Roy, Shrihari Sridharan, Shubham Jain, Anand Raghunathan
TxSim: Modeling Training of Deep Neural Networks on Resistive Crossbar Systems. CoRR (2020)
Shubham Jain, Sumeet Kumar Gupta, Anand Raghunathan
TiM-DNN: Ternary In-Memory Accelerator for Deep Neural Networks. IEEE Trans. Very Large Scale Integr. Syst. 28 (7) (2020)
Sanjay Ganapathy, Swagath Venkataramani, Giridhur Sriraman, Balaraman Ravindran, Anand Raghunathan
DyVEDeep: Dynamic Variable Effort Deep Neural Networks. ACM Trans. Embed. Comput. Syst. 19 (3) (2020)
Sourjya Roy, Priyadarshini Panda, Gopalakrishnan Srinivasan, Anand Raghunathan
Pruning Filters while Training for Efficiently Optimizing Deep Learning Networks. CoRR (2020)
Sai Aparna Aketi, Sourjya Roy, Anand Raghunathan, Kaushik Roy
Gradual Channel Pruning while Training using Feature Relevance Scores for Convolutional Neural Networks. CoRR (2020)
Sandeep Krishna Thirumala, Shubham Jain, Sumeet Kumar Gupta, Anand Raghunathan
Ternary Compute-Enabled Memory using Ferroelectric Transistors for Accelerating Deep Neural Networks. DATE (2020)
Amrit Nagarajan, Sanchari Sen, Jacob R. Stevens, Anand Raghunathan
Optimizing Transformers with Approximate Computing for Faster, Smaller and more Accurate NLP Models. CoRR (2020)
Indranil Chakraborty, Mustafa Fayez Ali, Aayush Ankit, Shubham Jain, Sourjya Roy, Shrihari Sridharan, Amogh Agrawal, Anand Raghunathan, Kaushik Roy
Resistive Crossbars as Approximate Hardware Building Blocks for Machine Learning: Opportunities and Challenges. Proc. IEEE 108 (12) (2020)
Sarada Krithivasan, Sanchari Sen, Anand Raghunathan
Adversarial Sparsity Attacks on Deep Neural Networks. CoRR (2020)
Sanchari Sen, Balaraman Ravindran, Anand Raghunathan
EMPIR: Ensembles of Mixed Precision Deep Networks for Increased Robustness Against Adversarial Attacks. ICLR (2020)
Vinod Ganesan, Sanchari Sen, Pratyush Kumar, Neel Gala, Kamakoti Veezhinathan, Anand Raghunathan
Sparsity-Aware Caches to Accelerate Deep Neural Networks. DATE (2020)
Vinod Ganesan, Surya Selvam, Sanchari Sen, Pratyush Kumar, Anand Raghunathan
A Case for Generalizable DNN Cost Models for Mobile Devices. IISWC (2020)
Sanchari Sen, Balaraman Ravindran, Anand Raghunathan
EMPIR: Ensembles of Mixed Precision Deep Networks for Increased Robustness against Adversarial Attacks. CoRR (2020)
Manik Singhal, Vijay Raghunathan, Anand Raghunathan
Communication-efficient View-Pooling for Distributed Multi-View Neural Networks. DATE (2020)
Sarada Krithivasan, Sanchari Sen, Anand Raghunathan
Sparsity Turns Adversarial: Energy and Latency Attacks on Deep Neural Networks. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 39 (11) (2020)
Swagath Venkataramani, Vivek Joy Kozhikkottu, Amit Sabne, Kaushik Roy, Anand Raghunathan
Logic Synthesis of Approximate Circuits. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 39 (10) (2020)
Sai Aparna Aketi, Sourjya Roy, Anand Raghunathan, Kaushik Roy
Gradual Channel Pruning While Training Using Feature Relevance Scores for Convolutional Neural Networks. IEEE Access 8 (2020)
David Brooks, Martin M. Frank, Tayfun Gokmen, Udit Gupta, Xiaobo Sharon Hu, Shubham Jain, Ann Franchesca Laguna, Michael T. Niemier, Ian O'Connor, Anand Raghunathan, Ashish Ranjan, Dayane Reis, Jacob R. Stevens, Carole-Jean Wu, Xunzhao Yin
Emerging Neural Workloads and Their Impact on Hardware. DATE (2020)
Sourjya Roy, Priyadarshini Panda, Gopalakrishnan Srinivasan, Anand Raghunathan
Pruning Filters while Training for Efficiently Optimizing Deep Learning Networks. IJCNN (2020)
Reena Elangovan, Shubham Jain, Anand Raghunathan
Ax-BxP: Approximate Blocked Computation for Precision-Reconfigurable Deep Neural Network Acceleration. CoRR (2020)
Shubham Jain, Anand Raghunathan
CxDNN: Hardware-software Compensation Methods for Deep Neural Networks on Resistive Crossbar Systems. ACM Trans. Embed. Comput. Syst. 18 (6) (2020)
Jacob R. Stevens, Ashish Ranjan, Dipankar Das, Bharat Kaul, Anand Raghunathan
Manna: An Accelerator for Memory-Augmented Neural Networks. MICRO (2019)
Sanchari Sen, Shubham Jain, Swagath Venkataramani, Anand Raghunathan
SparCE: Sparsity Aware General-Purpose Core Extensions to Accelerate Deep Neural Networks. IEEE Trans. Computers 68 (6) (2019)
Younghoon Kim, Swagath Venkataramani, Nitin Chandrachoodan, Anand Raghunathan
Data Subsetting: A Data-Centric Approach to Approximate Computing. DATE (2019)
Athindran Ramesh Kumar, Balaraman Ravindran, Anand Raghunathan
Pack and Detect: Fast Object Detection in Videos Using Region-of-Interest Packing. COMAD/CODS (2019)
Shubham Jain, Aayush Ankit, Indranil Chakraborty, Tayfun Gokmen, Malte J. Rasch, Wilfried Haensch, Kaushik Roy, Anand Raghunathan
Neural network accelerator design with resistive crossbars: Opportunities and challenges. IBM J. Res. Dev. 63 (6) (2019)
Sandeep Krishna Thirumala, Yi-Tse Hung, Shubham Jain, Arnab Raha, Niharika Thakuria, Vijay Raghunathan, Anand Raghunathan, Zhihong Chen, Sumeet Kumar Gupta
Valley-Coupled-Spintronic Non-Volatile Memories with Compute-In-Memory Support. CoRR (2019)
Sarada Krithivasan, Sanchari Sen, Swagath Venkataramani, Anand Raghunathan
Dynamic Spike Bundling for Energy-Efficient Spiking Neural Networks. ISLPED (2019)
Ashish Ranjan, Shubham Jain, Jacob R. Stevens, Dipankar Das, Bharat Kaul, Anand Raghunathan
X-MANN: A Crossbar based Architecture for Memory Augmented Neural Networks. DAC (2019)
Sandeep Krishna Thirumala, Shubham Jain, Anand Raghunathan, Sumeet Kumar Gupta
Non-Volatile Memory utilizing Reconfigurable Ferroelectric Transistors to enable Differential Read and Energy-Efficient In-Memory Computation. ISLPED (2019)
Shubham Jain, Sumeet Kumar Gupta, Anand Raghunathan
TiM-DNN: Ternary in-Memory accelerator for Deep Neural Networks. CoRR (2019)
Shubham Jain, Abhronil Sengupta, Kaushik Roy, Anand Raghunathan
Rx-Caffe: Framework for evaluating and training Deep Neural Networks on Resistive Crossbars. CoRR (2018)
Athindran Ramesh Kumar, Balaraman Ravindran, Anand Raghunathan
Pack and Detect: Fast Object Detection in Videos Using Region-of-Interest Packing. CoRR (2018)
Kyuin Lee, Vijay Raghunathan, Anand Raghunathan, Younghyun Kim
SYNCVIBE: Fast and Secure Device Pairing through Physical Vibration on Commodity Smartphones. ICCD (2018)
Shubham Jain, Sachin S. Sapatnekar, Jianping Wang, Kaushik Roy, Anand Raghunathan
Computing-in-memory with spintronics. DATE (2018)
Sanchari Sen, Anand Raghunathan
Approximate Computing for Long Short Term Memory (LSTM) Neural Networks. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 37 (11) (2018)
Shubham Jain, Ashish Ranjan, Kaushik Roy, Anand Raghunathan
Computing in Memory With Spin-Transfer Torque Magnetic RAM. IEEE Trans. Very Large Scale Integr. Syst. 26 (3) (2018)
Setareh Behroozi, Vijay Raghunathan, Anand Raghunathan, Younghyun Kim
A Quality-Configurable Approximate Serial Bus for Energy-Efficient Sensory Data Transfer. IEEE J. Emerg. Sel. Topics Circuits Syst. 8 (3) (2018)
Jacob R. Stevens, Ashish Ranjan, Anand Raghunathan
AxBA: an approximate bus architecture framework. ICCAD (2018)
Syed Shakib Sarwar, Gopalakrishnan Srinivasan, Bing Han, Parami Wijesinghe, Akhilesh Jaiswal, Priyadarshini Panda, Anand Raghunathan, Kaushik Roy
Energy Efficient Neural Computing: A Study of Cross-Layer Approximations. IEEE J. Emerg. Sel. Topics Circuits Syst. 8 (4) (2018)
Jacob R. Stevens, Yue Du, Vivek Kozhikkott, Anand Raghunathan
ACCLIB: Accelerators as libraries. DATE (2018)
Sybille Hellebrand, Jörg Henkel, Anand Raghunathan, Hans-Joachim Wunderlich
Guest Editors' Introduction. IEEE Embed. Syst. Lett. 10 (1) (2018)
Syed Shakib Sarwar, Swagath Venkataramani, Aayush Ankit, Anand Raghunathan, Kaushik Roy
Energy-Efficient Neural Computing with Approximate Multipliers. ACM J. Emerg. Technol. Comput. Syst. 14 (2) (2018)
Arsalan Mosenia, Susmita Sur-Kolay, Anand Raghunathan, Niraj K. Jha
Wearable Medical Sensor-Based System Design: A Survey. IEEE Trans. Multi Scale Comput. Syst. 3 (2) (2017)
Sanchari Sen, Shubham Jain, Swagath Venkataramani, Anand Raghunathan
SparCE: Sparsity aware General Purpose Core Extensions to Accelerate Deep Neural Networks. CoRR (2017)
Jianping Wang, Sachin S. Sapatnekar, Chris H. Kim, Paul A. Crowell, Steven J. Koester, Supriyo Datta, Kaushik Roy, Anand Raghunathan, Xiaobo Sharon Hu, Michael T. Niemier, Azad Naeemi, Chia-Ling Chien, Caroline A. Ross, Roland Kawakami
A Pathway to Enable Exponential Scaling for the Beyond-CMOS Era: Invited. DAC (2017)
Neel Gala, Swagath Venkataramani, Anand Raghunathan, V. Kamakoti
Approximate Error Detection With Stochastic Checkers. IEEE Trans. Very Large Scale Integr. Syst. 25 (8) (2017)
Younghyun Kim, Setareh Behroozi, Vijay Raghunathan, Anand Raghunathan
AXSERBUS: A quality-configurable approximate serial bus for energy-efficient sensing. ISLPED (2017)
Sanchari Sen, Swagath Venkataramani, Anand Raghunathan
Approximate computing for spiking neural networks. DATE (2017)
Arsalan Mosenia, Susmita Sur-Kolay, Anand Raghunathan, Niraj K. Jha
DISASTER: Dedicated Intelligent Security Attacks on Sensor-Triggered Emergency Responses. IEEE Trans. Multi Scale Comput. Syst. 3 (4) (2017)
Ashish Ranjan, Swagath Venkataramani, Zoha Pajouhi, Rangharajan Venkatesan, Kaushik Roy, Anand Raghunathan
STAxCache: An approximate, energy efficient STT-MRAM cache. DATE (2017)
Arnab Roy, Swagath Venkataramani, Neel Gala, Sanchari Sen, Kamakoti Veezhinathan, Anand Raghunathan
A Programmable Event-driven Architecture for Evaluating Spiking Neural Networks. ISLPED (2017)
Ashish Ranjan, Arnab Raha, Vijay Raghunathan, Anand Raghunathan
Approximate memory compression for energy-efficiency. ISLPED (2017)
Arsalan Mosenia, Susmita Sur-Kolay, Anand Raghunathan, Niraj K. Jha
CABA: Continuous Authentication Based on BioAura. IEEE Trans. Computers 66 (5) (2017)
Shubham Jain, Ashish Ranjan, Kaushik Roy, Anand Raghunathan
Computing in Memory with Spin-Transfer Torque Magnetic RAM. CoRR (2017)
Priyadarshini Panda, Swagath Venkataramani, Abhronil Sengupta, Anand Raghunathan, Kaushik Roy
Energy-Efficient Object Detection Using Semantic Decomposition. IEEE Trans. Very Large Scale Integr. Syst. 25 (9) (2017)
Sanjay Ganapathy, Swagath Venkataramani, Balaraman Ravindran, Anand Raghunathan
DyVEDeep: Dynamic Variable Effort Deep Neural Networks. CoRR (2017)
Swagath Venkataramani, Ashish Ranjan, Subarno Banerjee, Dipankar Das, Sasikanth Avancha, Ashok Jagannathan, Ajaya Durg, Dheemanth Nagaraj, Bharat Kaul, Pradeep Dubey, Anand Raghunathan
ScaleDeep: A Scalable Compute Architecture for Learning and Evaluating Deep Networks. ISCA (2017)
Arnab Raha, Swagath Venkataramani, Vijay Raghunathan, Anand Raghunathan
Energy-Efficient Reduce-and-Rank Using Input-Adaptive Approximations. IEEE Trans. Very Large Scale Integr. Syst. 25 (2) (2017)
Younghyun Kim, Vijay Raghunathan, Anand Raghunathan
Design and Management of Battery-Supercapacitor Hybrid Electrical Energy Storage Systems for Regulation Services. IEEE Trans. Multi Scale Comput. Syst. 3 (1) (2017)
Amit Sabne, Xiao Wang, Sherman J. Kisner, Charles A. Bouman, Anand Raghunathan, Samuel P. Midkiff
Model-based Iterative CT Image Reconstruction on GPUs. PPOPP (2017)
Shubham Jain, Swagath Venkataramani, Anand Raghunathan
Approximation through logic isolation for the design of quality configurable circuits. DATE (2016)

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Publication Activity (10 Years)

Top Topics

Top Venues

Anand Raghunathan