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Anand 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]