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ORCIDAnand Raghunathan
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- affiliation: Purdue University, West Lafayette, USA
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2020 – today
- 2024
[j120]Sarada Krithivasan, Sanchari Sen, Swagath Venkataramani, Anand Raghunathan:
MixTrain: accelerating DNN training via input mixing. Frontiers Artif. Intell. 7 (2024)- [j119]Abinand Nallathambi, Christin David Bose, Wilfried Haensch, Anand Raghunathan:
LRMP: Layer Replication with Mixed Precision for spatial in-memory DNN accelerators. Frontiers Artif. Intell. 7 (2024) - [j118]Surya Selvam
, Amrit Nagarajan, Anand Raghunathan:
Efficient Batched Inference in Conditional Neural Networks. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 43(11): 4081-4092 (2024) - [j117]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): 6:1-6:38 (2024) - [c219]Shrihari Sridharan, Surya Selvam, Kaushik Roy, Anand Raghunathan:
Ev-Edge: Efficient Execution of Event-based Vision Algorithms on Commodity Edge Platforms. DAC 2024: 336:1-336:6 - [c218]Amrit Nagarajan, Anand Raghunathan:
Input Compression with Positional Consistency for Efficient Training and Inference of Transformer Neural Networks. ECML/PKDD (5) 2024: 73-88 - [i33]Shrihari Sridharan, Surya Selvam, Kaushik Roy, Anand Raghunathan:
Ev-Edge: Efficient Execution of Event-based Vision Algorithms on Commodity Edge Platforms. CoRR abs/2403.15717 (2024) - [i32]Niharika Thakuria, Akul Malhotra, Sandeep Krishna Thirumala, Reena Elangovan, Anand Raghunathan, Sumeet Kumar Gupta:
SiTe CiM: Signed Ternary Computing-in-Memory for Ultra-Low Precision Deep Neural Networks. CoRR abs/2408.13617 (2024) - [i31]Jimmy Gammell, Anand Raghunathan, Kaushik Roy:
Power side-channel leakage localization through adversarial training of deep neural networks. CoRR abs/2410.22425 (2024) - 2023
- [j116]Amrit Nagarajan, Anand Raghunathan:
FASTRAIN-GNN: Fast and Accurate Self-Training for Graph Neural Networks. Trans. Mach. Learn. Res. 2023 (2023) - [j115]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): 1223-1233 (2023) - [c217]Amrit Nagarajan, Anand Raghunathan:
TokenDrop + BucketSampler: Towards Efficient Padding-free Fine-tuning of Language Models. EMNLP (Findings) 2023: 11682-11695 - [c216]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: 1-6 - [i30]Shrihari Sridharan, Jacob R. Stevens, Kaushik Roy, Anand Raghunathan:
X-Former: In-Memory Acceleration of Transformers. CoRR abs/2303.07470 (2023) - [i29]Sourjya Roy, Cheng Wang, Anand Raghunathan:
Evaluation of STT-MRAM as a Scratchpad for Training in ML Accelerators. CoRR abs/2308.02024 (2023) - [i28]Abinand Nallathambi, Christin David Bose, Wilfried Haensch, Anand Raghunathan:
LRMP: Layer Replication with Mixed Precision for Spatial In-memory DNN Accelerators. CoRR abs/2312.03146 (2023) - [i27]Amrit Nagarajan, Anand Raghunathan:
Input Compression with Positional Consistency for Efficient Training and Inference of Transformer Neural Networks. CoRR abs/2312.12385 (2023) - 2022
- [j114]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): 55:1-55:29 (2022) - [j113]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): 28:1-28:20 (2022) - [j112]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): 1615-1630 (2022) - [c215]Amrit Nagarajan, Jacob R. Stevens, Anand Raghunathan:
Efficient ensembles of graph neural networks. DAC 2022: 187-192 - [c214]Sarada Krithivasan, Sanchari Sen, Nitin Rathi, Kaushik Roy, Anand Raghunathan:
Efficiency attacks on spiking neural networks. DAC 2022: 373-378 - [c213]Gobinda Saha, Cheng Wang, Anand Raghunathan, Kaushik Roy:
A cross-layer approach to cognitive computing: invited. DAC 2022: 1327-1330 - [c212]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: 80:1-80:9 - [c211]Aradhana Mohan Parvathy, Sarada Krithivasan, Sanchari Sen, Anand Raghunathan:
Seprox: Sequence-Based Approximations for Compressing Ultra-Low Precision Deep Neural Networks. ICCAD 2022: 153:1-153:9 - [c210]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: 1-8 - [c209]Abinand Nallathambi, Sanchari Sen, Anand Raghunathan, Nitin Chandrachoodan:
Layerwise Disaggregated Evaluation of Spiking Neural Networks. ISLPED 2022: 25:1-25:6 - [c208]Reena Elangovan, Ashish Ranjan, Niharika Thakuria, Sumeet Kumar Gupta, Anand Raghunathan:
Energy Efficient Cache Design with Piezoelectric FETs. ISLPED 2022: 31:1-31:6 - [p3]Sourav Sanyal, Shubham Negi, Anand Raghunathan, Kaushik Roy:
Approximate Computing for Machine Learning Workloads: A Circuits and Systems Perspective. Approximate Computing 2022: 365-395 - [i26]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 abs/2203.16416 (2022) - [i25]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 abs/2206.08735 (2022) - [i24]Jörg Henkel, Hai Li, Anand Raghunathan, Mehdi B. Tahoori, Swagath Venkataramani, Xiaoxuan Yang, Georgios Zervakis:
Approximate Computing and the Efficient Machine Learning Expedition. CoRR abs/2210.00497 (2022) - 2021
- [j111]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): 701-710 (2021) - [j110]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): 326-338 (2021) - [j109]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): 730-738 (2021) - [c207]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: 1-4 - [c206]Basar Kütükçü, Sabur Baidya, Anand Raghunathan, Sujit Dey:
Contention-aware Adaptive Model Selection for Machine Vision in Embedded Systems. AICAS 2021: 1-4 - [c205]Jacob R. Stevens, Rangharajan Venkatesan, Steve Dai, Brucek Khailany, Anand Raghunathan:
Softermax: Hardware/Software Co-Design of an Efficient Softmax for Transformers. DAC 2021: 469-474 - [c204]Jacob R. Stevens, Dipankar Das, Sasikanth Avancha, Bharat Kaul, Anand Raghunathan:
GNNerator: A Hardware/Software Framework for Accelerating Graph Neural Networks. DAC 2021: 955-960 - [c203]Younghoon Kim, Swagath Venkataramani, Sanchari Sen, Anand Raghunathan:
Value Similarity Extensions for Approximate Computing in General-Purpose Processors. DATE 2021: 481-486 - [c202]Sanchari Sen, Swagath Venkataramani, Anand Raghunathan:
Efficacy of Pruning in Ultra-Low Precision DNNs. ISLPED 2021: 1-6 - [i23]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 abs/2103.01781 (2021) - [i22]Jacob R. Stevens, Rangharajan Venkatesan, Steve Dai, Brucek Khailany, Anand Raghunathan:
Softermax: Hardware/Software Co-Design of an Efficient Softmax for Transformers. CoRR abs/2103.09301 (2021) - [i21]Jacob R. Stevens, Dipankar Das, Sasikanth Avancha, Bharat Kaul, Anand Raghunathan:
GNNerator: A Hardware/Software Framework for Accelerating Graph Neural Networks. CoRR abs/2103.10836 (2021) - [i20]Sourjya Roy, Mustafa Fayez Ali, Anand Raghunathan:
PIM-DRAM: Accelerating Machine Learning Workloads using Processing in Commodity DRAM. CoRR abs/2105.03736 (2021) - 2020
- [j108]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: 171924-171932 (2020) - [j107]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): 2276-2310 (2020) - [j106]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): 2503-2515 (2020) - [j105]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): 4129-4141 (2020) - [j104]Shubham Jain, Anand Raghunathan:
CxDNN: Hardware-software Compensation Methods for Deep Neural Networks on Resistive Crossbar Systems. ACM Trans. Embed. Comput. Syst. 18(6): 113:1-113:23 (2020) - [j103]Sanjay Ganapathy, Swagath Venkataramani, Giridhur Sriraman, Balaraman Ravindran, Anand Raghunathan:
DyVEDeep: Dynamic Variable Effort Deep Neural Networks. ACM Trans. Embed. Comput. Syst. 19(3): 16:1-16:24 (2020) - [j102]Ashish Ranjan, Arnab Raha, Vijay Raghunathan, Anand Raghunathan:
Approximate Memory Compression. IEEE Trans. Very Large Scale Integr. Syst. 28(4): 980-991 (2020) - [j101]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): 1567-1577 (2020) - [c201]Sandeep Krishna Thirumala, Shubham Jain, Sumeet Kumar Gupta, Anand Raghunathan:
Ternary Compute-Enabled Memory using Ferroelectric Transistors for Accelerating Deep Neural Networks. DATE 2020: 31-36 - [c200]Vinod Ganesan, Sanchari Sen, Pratyush Kumar, Neel Gala, Kamakoti Veezhinathan, Anand Raghunathan:
Sparsity-Aware Caches to Accelerate Deep Neural Networks. DATE 2020: 85-90 - [c199]Manik Singhal, Vijay Raghunathan, Anand Raghunathan:
Communication-efficient View-Pooling for Distributed Multi-View Neural Networks. DATE 2020: 1390-1395 - [c198]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: 1462-1471 - [c197]Sanchari Sen, Balaraman Ravindran, Anand Raghunathan:
EMPIR: Ensembles of Mixed Precision Deep Networks for Increased Robustness Against Adversarial Attacks. ICLR 2020 - [c196]Vinod Ganesan, Surya Selvam, Sanchari Sen, Pratyush Kumar, Anand Raghunathan:
A Case for Generalizable DNN Cost Models for Mobile Devices. IISWC 2020: 169-180 - [c195]Sourjya Roy, Priyadarshini Panda, Gopalakrishnan Srinivasan, Anand Raghunathan:
Pruning Filters while Training for Efficiently Optimizing Deep Learning Networks. IJCNN 2020: 1-7 - [i19]Sai Aparna Aketi, Sourjya Roy, Anand Raghunathan, Kaushik Roy:
Gradual Channel Pruning while Training using Feature Relevance Scores for Convolutional Neural Networks. CoRR abs/2002.09958 (2020) - [i18]Sourjya Roy, Shrihari Sridharan, Shubham Jain, Anand Raghunathan:
TxSim: Modeling Training of Deep Neural Networks on Resistive Crossbar Systems. CoRR abs/2002.11151 (2020) - [i17]Sourjya Roy, Priyadarshini Panda, Gopalakrishnan Srinivasan, Anand Raghunathan:
Pruning Filters while Training for Efficiently Optimizing Deep Learning Networks. CoRR abs/2003.02800 (2020) - [i16]Sanchari Sen, Balaraman Ravindran, Anand Raghunathan:
EMPIR: Ensembles of Mixed Precision Deep Networks for Increased Robustness against Adversarial Attacks. CoRR abs/2004.10162 (2020) - [i15]Sarada Krithivasan, Sanchari Sen, Anand Raghunathan:
Adversarial Sparsity Attacks on Deep Neural Networks. CoRR abs/2006.08020 (2020) - [i14]Amrit Nagarajan, Sanchari Sen, Jacob R. Stevens, Anand Raghunathan:
Optimizing Transformers with Approximate Computing for Faster, Smaller and more Accurate NLP Models. CoRR abs/2010.03688 (2020) - [i13]Reena Elangovan, Shubham Jain, Anand Raghunathan:
Ax-BxP: Approximate Blocked Computation for Precision-Reconfigurable Deep Neural Network Acceleration. CoRR abs/2011.13000 (2020)
2010 – 2019
- 2019
- [j100]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): 10:1-10:13 (2019) - [j99]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): 912-925 (2019) - [c194]Athindran Ramesh Kumar, Balaraman Ravindran, Anand Raghunathan:
Pack and Detect: Fast Object Detection in Videos Using Region-of-Interest Packing. COMAD/CODS 2019: 150-156 - [c193]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: 130 - [c192]Younghoon Kim, Swagath Venkataramani, Nitin Chandrachoodan, Anand Raghunathan:
Data Subsetting: A Data-Centric Approach to Approximate Computing. DATE 2019: 576-581 - [c191]Sarada Krithivasan, Sanchari Sen, Swagath Venkataramani, Anand Raghunathan:
Dynamic Spike Bundling for Energy-Efficient Spiking Neural Networks. ISLPED 2019: 1-6 - [c190]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: 1-6 - [c189]Jacob R. Stevens, Ashish Ranjan, Dipankar Das, Bharat Kaul, Anand Raghunathan:
Manna: An Accelerator for Memory-Augmented Neural Networks. MICRO 2019: 794-806 - [p2]Ashish Ranjan, Swagath Venkataramani, Shubham Jain, Younghoon Kim, Shankar Ganesh Ramasubramanian, Arnab Raha, Kaushik Roy, Anand Raghunathan:
Automatic Synthesis Techniques for Approximate Circuits. Approximate Circuits 2019: 123-140 - [i12]Shubham Jain, Sumeet Kumar Gupta, Anand Raghunathan:
TiM-DNN: Ternary in-Memory accelerator for Deep Neural Networks. CoRR abs/1909.06892 (2019) - [i11]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 abs/1912.07821 (2019) - 2018
- [j98]Sybille Hellebrand, Jörg Henkel, Anand Raghunathan, Hans-Joachim Wunderlich:
Guest Editors' Introduction. IEEE Embed. Syst. Lett. 10(1): 1 (2018) - [j97]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): 379-390 (2018) - [j96]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): 796-809 (2018) - [j95]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): 16:1-16:23 (2018) - [j94]Sanchari Sen, Anand Raghunathan:
Approximate Computing for Long Short Term Memory (LSTM) Neural Networks. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 37(11): 2266-2276 (2018) - [j93]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): 470-483 (2018) - [c188]Jacob R. Stevens, Yue Du, Vivek Kozhikkott, Anand Raghunathan:
ACCLIB: Accelerators as libraries. DATE 2018: 245-248 - [c187]Shubham Jain, Sachin S. Sapatnekar, Jianping Wang, Kaushik Roy, Anand Raghunathan:
Computing-in-memory with spintronics. DATE 2018: 1640-1645 - [c186]Jacob R. Stevens, Ashish Ranjan, Anand Raghunathan:
AxBA: an approximate bus architecture framework. ICCAD 2018: 43 - [c185]Kyuin Lee, Vijay Raghunathan, Anand Raghunathan, Younghyun Kim:
SYNCVIBE: Fast and Secure Device Pairing through Physical Vibration on Commodity Smartphones. ICCD 2018: 234-241 - [i10]Shubham Jain, Abhronil Sengupta, Kaushik Roy, Anand Raghunathan:
Rx-Caffe: Framework for evaluating and training Deep Neural Networks on Resistive Crossbars. CoRR abs/1809.00072 (2018) - [i9]Athindran Ramesh Kumar, Balaraman Ravindran, Anand Raghunathan:
Pack and Detect: Fast Object Detection in Videos Using Region-of-Interest Packing. CoRR abs/1809.01701 (2018) - 2017
- [j92]Arsalan Mosenia, Susmita Sur-Kolay, Anand Raghunathan, Niraj K. Jha:
CABA: Continuous Authentication Based on BioAura. IEEE Trans. Computers 66(5): 759-772 (2017) - [j91]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): 12-24 (2017) - [j90]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): 124-138 (2017) - [j89]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): 255-268 (2017) - [j88]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): 462-475 (2017) - [j87]Neel Gala, Swagath Venkataramani, Anand Raghunathan, V. Kamakoti:
Approximate Error Detection With Stochastic Checkers. IEEE Trans. Very Large Scale Integr. Syst. 25(8): 2258-2270 (2017) - [j86]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): 2673-2677 (2017) - [c184]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: 16:1-16:6 - [c183]Sanchari Sen, Swagath Venkataramani, Anand Raghunathan:
Approximate computing for spiking neural networks. DATE 2017: 193-198 - [c182]Ashish Ranjan, Swagath Venkataramani, Zoha Pajouhi, Rangharajan Venkatesan, Kaushik Roy, Anand Raghunathan:
STAxCache: An approximate, energy efficient STT-MRAM cache. DATE 2017: 356-361 - [c181]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: 13-26 - [c180]Younghyun Kim, Setareh Behroozi, Vijay Raghunathan, Anand Raghunathan:
AXSERBUS: A quality-configurable approximate serial bus for energy-efficient sensing. ISLPED 2017: 1-6 - [c179]Ashish Ranjan, Arnab Raha, Vijay Raghunathan, Anand Raghunathan:
Approximate memory compression for energy-efficiency. ISLPED 2017: 1-6 - [c178]Arnab Roy, Swagath Venkataramani, Neel Gala, Sanchari Sen, Kamakoti Veezhinathan, Anand Raghunathan:
A Programmable Event-driven Architecture for Evaluating Spiking Neural Networks. ISLPED 2017: 1-6 - [c177]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: 207-220 - [i8]Shubham Jain, Ashish Ranjan, Kaushik Roy, Anand Raghunathan:
Computing in Memory with Spin-Transfer Torque Magnetic RAM. CoRR abs/1703.02118 (2017) - [i7]Sanjay Ganapathy, Swagath Venkataramani, Balaraman Ravindran, Anand Raghunathan:
DyVEDeep: Dynamic Variable Effort Deep Neural Networks. CoRR abs/1704.01137 (2017) - [i6]Sanchari Sen, Shubham Jain, Swagath Venkataramani, Anand Raghunathan:
SparCE: Sparsity aware General Purpose Core Extensions to Accelerate Deep Neural Networks. CoRR abs/1711.06315 (2017) - 2016
- [j85]Kaushik Roy, Byunghoo Jung, Dimitrios Peroulis, Anand Raghunathan:
Integrated Systems in the More-Than-Moore Era: Designing Low-Cost Energy-Efficient Systems Using Heterogeneous Components. IEEE Des. Test 33(3): 56-65 (2016) - [j84]Zoha Pajouhi, Xuanyao Fong, Anand Raghunathan, Kaushik Roy:
Yield, Area, and Energy Optimization in STT-MRAMs Using Failure-Aware ECC. ACM J. Emerg. Technol. Comput. Syst. 13(2): 20:1-20:20 (2016) - [j83]A. Arun Goud, Rangharajan Venkatesan, Anand Raghunathan, Kaushik Roy:
Asymmetric Underlapped FinFETs for Near- and Super-Threshold Logic at Sub-10nm Technology Nodes. ACM J. Emerg. Technol. Comput. Syst. 13(2): 23:1-23:22 (2016) - [j82]Xuanyao Fong, Yusung Kim, Rangharajan Venkatesan, Sri Harsha Choday, Anand Raghunathan, Kaushik Roy:
Spin-Transfer Torque Memories: Devices, Circuits, and Systems. Proc. IEEE 104(7): 1449-1488 (2016) - [j81]Rangharajan Venkatesan, Vivek Joy Kozhikkottu, Mrigank Sharad, Charles Augustine, Arijit Raychowdhury, Kaushik Roy, Anand Raghunathan:
Cache Design with Domain Wall Memory. IEEE Trans. Computers 65(4): 1010-1024 (2016) - [j80]Xuanyao Fong, Yusung Kim, Karthik Yogendra, Deliang Fan, Abhronil Sengupta, Anand Raghunathan, Kaushik Roy:
Spin-Transfer Torque Devices for Logic and Memory: Prospects and Perspectives. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 35(1): 1-22 (2016) - [j79]Arsalan Mohsen Nia, Susmita Sur-Kolay, Anand Raghunathan, Niraj K. Jha:
Physiological Information Leakage: A New Frontier in Health Information Security. IEEE Trans. Emerg. Top. Comput. 4(3): 321-334 (2016) - [j78]