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2020 – today
- 2024
[c46]Maruthi Seshidhar Inukonda, Krishna Prashanth Thummanapelly, Bheemarjuna Reddy Tamma, Sparsh Mittal:
TEFAR: An Efficient Transparent Finer-Grained Encryption of Internet Access Artifacts. COMSNETS 2024: 1164-1169- [c45]Onkar Susladkar, Gayatri Deshmukh, Vandan Gorade, Sparsh Mittal:
GRIZAL: Generative Prior-guided Zero-Shot Temporal Action Localization. EMNLP 2024: 19046-19059 - [c44]Vandan Gorade, Sparsh Mittal, Debesh Jha, Ulas Bagci:
Rethinking Intermediate Layers Design in Knowledge Distillation for Kidney and Liver Tumor Segmentation. ISBI 2024: 1-6 - [c43]Vishesh Mishra
, Sparsh Mittal, Nirbhay Mishra, Rekha Singhal:
Security Implications of Approximation: A Study of Trojan Attacks on Approximate Adders and Multipliers. VLSID 2024: 511-516 - [c42]Gayatri Deshmukh, Onkar Susladkar, Dhruv Makwana, Sparsh Mittal, R. Sai Chandra Teja:
Textual Alchemy: CoFormer for Scene Text Understanding. WACV 2024: 2919-2929 - [c41]Dhruv Makwana, Gayatri Deshmukh, Onkar Susladkar, Sparsh Mittal, R. Sai Chandra Teja:
LIVENet: A novel network for real-world low-light image denoising and enhancement. WACV 2024: 5844-5853 - [c40]Vandan Gorade, Sparsh Mittal, Debesh Jha, Ulas Bagci:
SynergyNet: Bridging the Gap between Discrete and Continuous Representations for Precise Medical Image Segmentation. WACV 2024: 7753-7762 - [i33]Vandan Gorade, Sparsh Mittal, Debesh Jha, Rekha Singhal, Ulas Bagci:
Harmonized Spatial and Spectral Learning for Robust and Generalized Medical Image Segmentation. CoRR abs/2401.10373 (2024) - [i32]Onkar Susladkar, Gayatri Deshmukh, Sparsh Mittal, Parth Shastri:
D2Styler: Advancing Arbitrary Style Transfer with Discrete Diffusion Methods. CoRR abs/2408.03558 (2024) - [i31]Sparsh Mittal, Yash Chand, Neel Kanth Kundu:
Hybrid Quantum Neural Network based Indoor User Localization using Cloud Quantum Computing. CoRR abs/2410.00708 (2024) - [i30]Onkar Susladkar, Jishu Sen Gupta, Chirag Sehgal, Sparsh Mittal, Rekha Singhal:
MotionAura: Generating High-Quality and Motion Consistent Videos using Discrete Diffusion. CoRR abs/2410.07659 (2024) - [i29]Ashwini Malviya, Sparsh Mittal:
Comparison of Machine Learning Approaches for Classifying Spinodal Events. CoRR abs/2410.09756 (2024) - 2023
- [j74]Vandan Gorade, Sparsh Mittal, Rekha Singhal:
PaCL: Patient-aware contrastive learning through metadata refinement for generalized early disease diagnosis. Comput. Biol. Medicine 167: 107569 (2023) - [j73]Dhruv Makwana, R. Sai Chandra Teja, Sparsh Mittal:
PCBSegClassNet - A light-weight network for segmentation and classification of PCB component. Expert Syst. Appl. 225: 120029 (2023) - [j72]Aabid Amin Fida, Farooq Ahmad Khanday, Sparsh Mittal:
An active memristor based rate-coded spiking neural network. Neurocomputing 533: 61-71 (2023) - [j71]Krishna Teja Chitty-Venkata, Sparsh Mittal, Murali Emani, Venkatram Vishwanath, Arun K. Somani:
A survey of techniques for optimizing transformer inference. J. Syst. Archit. 144: 102990 (2023) - [j70]Jens Domke, Emil Vatai, Balazs Gerofi, Yuetsu Kodama, Mohamed Wahib, Artur Podobas, Sparsh Mittal, Miquel Pericàs, Lingqi Zhang, Peng Chen, Aleksandr Drozd, Satoshi Matsuoka:
At the Locus of Performance: Quantifying the Effects of Copious 3D-Stacked Cache on HPC Workloads. ACM Trans. Archit. Code Optim. 20(4): 57:1-57:26 (2023) - [j69]Vishesh Mishra, Sparsh Mittal, Neelofar Hassan, Rekha Singhal, Urbi Chatterjee:
VADF: Versatile Approximate Data Formats for Energy-Efficient Computing. ACM Trans. Embed. Comput. Syst. 22(5s): 111:1-111:21 (2023) - [j68]Sparsh Mittal, Srishti Srivastava, J. Phani Jayanth:
A Survey of Deep Learning Techniques for Underwater Image Classification. IEEE Trans. Neural Networks Learn. Syst. 34(10): 6968-6982 (2023) - [c39]Maruthi Seshidhar Inukonda, Jatin Tarachandani, Imtiaz Ahmed, Bheemarjuna Reddy Tamma, Sparsh Mittal:
ZETA: A Zero-Trust Security based Forensic-Ready Solution for Perimeter-less Enterprise Networks. ANTS 2023: 189-194 - [c38]Onkar Susladkar, Gayatri S. Deshmukh, Sparsh Mittal, R. Sai Chandra Teja, Rekha Singhal:
LiBERTy: A Novel Model for Natural Language Understanding. AIMLSystems 2023: 1:1-1:9 - [c37]Aaditi Kapre, Shruti Kunde, Sparsh Mittal, Rekha Singhal:
RAxC: Reflexivity-based Approximate Computing techniques for efficient remote sensing. IEEE Big Data 2023: 1168-1173 - [c36]Maruthi Seshidhar Inukonda, Sai Harsha Kottapalli, Bheemarjuna Reddy Tamma, Sparsh Mittal:
FENCE: A Real-Time Privacy-Preserving Solution for Enterprise Internet Forensics at Scale. COMSNETS 2023: 174-176 - [c35]Onkar Susladkar, Dhruv Makwana, Gayatri Deshmukh, Sparsh Mittal, R. Sai Chandra Teja, Rekha Singhal:
TPFNet: A Novel Text In-painting Transformer for Text Removal. ICDAR (6) 2023: 155-172 - [c34]Yash Khare, Kumud Lakara, Sparsh Mittal, Arvind Kaushik, Rekha Singhal:
SpotOn: A Gradient-based Targeted Data Poisoning Attack on Deep Neural Networks. ISQED 2023: 1-8 - [c33]Ananya Mantravadi, Dhruv Makwana, R. Sai Chandra Teja, Sparsh Mittal, Rekha Singhal:
Dilated Involutional Pyramid Network (DInPNet): A Novel Model for Printed Circuit Board (PCB) Components Classification. ISQED 2023: 1-7 - [c32]Vishesh Mishra, Sparsh Mittal, Rekha Singhal, Manoj Nambiar:
Novel, Configurable Approximate Floating-point Multipliers for Error-Resilient Applications. ISQED 2023: 1-7 - [c31]Vishu Saxena, Yash Jain, Sparsh Mittal:
Machine Learning and Polynomial Chaos models for Accurate Prediction of SET Pulse Current. ISVLSI 2023: 1-6 - [c30]Vibhu, Sparsh Mittal, Vivek Kumar:
Machine Learning-based model for Single Event Upset Current Prediction in 14nm FinFETs. VLSID 2023: 1-6 - [c29]Onkar Susladkar, Gayatri Deshmukh, Dhruv Makwana, Sparsh Mittal, R. Sai Chandra Teja, Rekha Singhal:
GAFNet: A Global Fourier Self Attention Based Novel Network for multi-modal downstream tasks. WACV 2023: 5231-5240 - [i28]Krishna Teja Chitty-Venkata, Sparsh Mittal, Murali Emani, Venkatram Vishwanath, Arun K. Somani:
A Survey of Techniques for Optimizing Transformer Inference. CoRR abs/2307.07982 (2023) - [i27]Vandan Gorade, Sparsh Mittal, Debesh Jha, Ulas Bagci:
SynergyNet: Bridging the Gap between Discrete and Continuous Representations for Precise Medical Image Segmentation. CoRR abs/2310.17764 (2023) - [i26]Vandan Gorade, Sparsh Mittal, Debesh Jha, Ulas Bagci:
Rethinking Intermediate Layers design in Knowledge Distillation for Kidney and Liver Tumor Segmentation. CoRR abs/2311.16700 (2023) - [i25]Tushir Sahu, Vidhi Bhatt, R. Sai Chandra Teja, Sparsh Mittal, Nagesh Kumar S:
SPEEDNet: Salient Pyramidal Enhancement Encoder-Decoder Network for Colonoscopy Images. CoRR abs/2312.01128 (2023) - 2022
- [j67]Maruthi Seshidhar Inukonda, Atharva Rajendra Karpate, Bheemarjuna Reddy Tamma, Sparsh Mittal, Praveen Tammana:
NASCENT: A Non-Invasive Solution for Detecting Utilization of Servers in Bare-Metal Cloud. IEEE Access 10: 12866-12881 (2022) - [j66]Subhrajit Nag, Dhruv Makwana, R. Sai Chandra Teja, Sparsh Mittal, C. Krishna Mohan:
WaferSegClassNet - A light-weight network for classification and segmentation of semiconductor wafer defects. Comput. Ind. 142: 103720 (2022) - [j65]Onkar Susladkar, Gayatri Deshmukh, Subhrajit Nag, Ananya Mantravadi, Dhruv Makwana, Sujitha Ravichandran, R. Sai Chandra Teja, Gajanan H. Chavhan, C. Krishna Mohan, Sparsh Mittal:
ClarifyNet: A high-pass and low-pass filtering based CNN for single image dehazing. J. Syst. Archit. 132: 102736 (2022) - [j64]Sheel Sindhu Manohar, Sparsh Mittal, Hemangee K. Kapoor:
CORIDOR: Using COherence and TempoRal LocalIty to Mitigate Read Disurbance ErrOR in STT-RAM Caches. ACM Trans. Embed. Comput. Syst. 21(1): 2:1-2:24 (2022) - [j63]Sparsh Mittal, Poonam Rajput, Sreenivas Subramoney:
A Survey of Deep Learning on CPUs: Opportunities and Co-Optimizations. IEEE Trans. Neural Networks Learn. Syst. 33(10): 5095-5115 (2022) - [c28]Yash Jain, Vishu Saxena, Sparsh Mittal:
Ensembling Deep Learning And CIELAB Color Space Model for Fire Detection from UAV images. AIMLSystems 2022: 7:1-7:9 - [c27]Yash Khare, Kumud Lakara, Maruthi Seshidhar Inukonda, Sparsh Mittal, Mahesh Chandra, Arvind Kaushik:
Design and Analysis of Novel Bit-flip Attacks and Defense Strategies for DNNs. DSC 2022: 1-8 - [c26]Sudhir Kumar Rai, Ashish Mittal, Sparsh Mittal:
A Node-Embedding Features Based Machine Learning Technique for Dynamic Malware Detection. DSC 2022: 1-8 - [c25]Vishesh Mishra, Sparsh Mittal, Saurabh Singh, Divy Pandey, Rekha Singhal:
MEGA-MAC: A Merged Accumulation based Approximate MAC Unit for Error Resilient Applications. ACM Great Lakes Symposium on VLSI 2022: 325-328 - [i24]Jens Domke, Emil Vatai, Balazs Gerofi, Yuetsu Kodama, Mohamed Wahib, Artur Podobas, Sparsh Mittal, Miquel Pericàs, Lingqi Zhang, Peng Chen, Aleksandr Drozd, Satoshi Matsuoka:
At the Locus of Performance: A Case Study in Enhancing CPUs with Copious 3D-Stacked Cache. CoRR abs/2204.02235 (2022) - [i23]Subhrajit Nag, Dhruv Makwana, R. Sai Chandra Teja, Sparsh Mittal, C. Krishna Mohan:
WaferSegClassNet - A Light-weight Network for Classification and Segmentation of Semiconductor Wafer Defects. CoRR abs/2207.00960 (2022) - [i22]Dhruv Makwana, Subhrajit Nag, Onkar Susladkar, Gayatri Deshmukh, R. Sai Chandra Teja, Sparsh Mittal, C. Krishna Mohan:
ACLNet: An Attention and Clustering-based Cloud Segmentation Network. CoRR abs/2207.06277 (2022) - [i21]Onkar Susladkar, Dhruv Makwana, Gayatri Deshmukh, Sparsh Mittal, R. Sai Chandra Teja, Rekha Singhal:
TPFNet: A Novel Text In-painting Transformer for Text Removal. CoRR abs/2210.14461 (2022) - 2021
- [j62]Sparsh Mittal, Sumanth Umesh:
A survey On hardware accelerators and optimization techniques for RNNs. J. Syst. Archit. 112: 101839 (2021) - [j61]Sumanth Umesh, Sparsh Mittal:
A survey of techniques for intermittent computing. J. Syst. Archit. 112: 101859 (2021) - [j60]Sparsh Mittal, Vibhu:
A survey of accelerator architectures for 3D convolution neural networks. J. Syst. Archit. 115: 102041 (2021) - [j59]Santanu Pattanayak, Subhrajit Nag, Sparsh Mittal:
CURATING: A multi-objective based pruning technique for CNNs. J. Syst. Archit. 116: 102031 (2021) - [j58]Srishti Srivastava, Sarthak Narayan, Sparsh Mittal:
A survey of deep learning techniques for vehicle detection from UAV images. J. Syst. Archit. 117: 102152 (2021) - [j57]Sparsh Mittal, Himanshi Gupta, Srishti Srivastava:
A survey on hardware security of DNN models and accelerators. J. Syst. Archit. 117: 102163 (2021) - [j56]Nivedita Shrivastava, Muhammad Abdullah Hanif, Sparsh Mittal, Smruti Ranjan Sarangi, Muhammad Shafique:
A survey of hardware architectures for generative adversarial networks. J. Syst. Archit. 118: 102227 (2021) - [j55]Sparsh Mittal, Gaurav Verma, Brajesh Kumar Kaushik, Farooq Ahmad Khanday:
A survey of SRAM-based in-memory computing techniques and applications. J. Syst. Archit. 119: 102276 (2021) - [j54]Nandan Kumar Jha, Sparsh Mittal:
Modeling Data Reuse in Deep Neural Networks by Taking Data-Types into Cognizance. IEEE Trans. Computers 70(9): 1526-1538 (2021) - [c24]Saksham Sharma, Vanshika V. Bhargava, Aditya Kumar Singh, Kshitij Bhardwaj, Sparsh Mittal:
Leveraging Prediction Confidence For Versatile Optimizations to CNNs. AIMLSystems 2021: 2:1-2:7 - [c23]Bhargav Achary Dandpati Kumar, R. Sai Chandra Teja, Sparsh Mittal, Biswabandan Panda, C. Krishna Mohan:
Inferring DNN layer-types through a Hardware Performance Counters based Side Channel Attack. AIMLSystems 2021: 4:1-4:7 - 2020
- [j53]Sparsh Mittal:
A survey on evaluating and optimizing performance of Intel Xeon Phi. Concurr. Comput. Pract. Exp. 32(19) (2020) - [j52]Nandan Kumar Jha, Sparsh Mittal, Binod Kumar, Govardhan Mattela:
DeepPeep: Exploiting Design Ramifications to Decipher the Architecture of Compact DNNs. ACM J. Emerg. Technol. Comput. Syst. 17(1): 5:1-5:25 (2020) - [j51]Sparsh Mittal:
A survey on modeling and improving reliability of DNN algorithms and accelerators. J. Syst. Archit. 104: 101689 (2020) - [j50]Sparsh Mittal:
A survey of FPGA-based accelerators for convolutional neural networks. Neural Comput. Appl. 32(4): 1109-1139 (2020) - [c22]Poonam Rajput, Sparsh Mittal, Sarthak Narayan:
Improving Accuracy and Efficiency of Object Detection Algorithms Using Multiscale Feature Aggregation Plugins. ANNPR 2020: 65-76 - [c21]Poonam Rajput, Subhrajit Nag, Sparsh Mittal:
Detecting Usage of Mobile Phones using Deep Learning Technique. GOODTECHS 2020: 96-101 - [c20]Nandan Kumar Jha, Shreyas Ravishankar, Sparsh Mittal, Arvind Kaushik, Dipan Mandal, Mahesh Chandra:
DRACO: Co-Optimizing Hardware Utilization, and Performance of DNNs on Systolic Accelerator. ISVLSI 2020: 574-579 - [c19]Nandan Kumar Jha, Rajat Saini, Subhrajit Nag, Sparsh Mittal:
E2GC: Energy-efficient Group Convolution in Deep Neural Networks. VLSID 2020: 155-160 - [c18]Rajat Saini, Nandan Kumar Jha, Bedanta Das, Sparsh Mittal, C. Krishna Mohan:
ULSAM: Ultra-Lightweight Subspace Attention Module for Compact Convolutional Neural Networks. WACV 2020: 1616-1625 - [i20]Nandan Kumar Jha, Sparsh Mittal, Govardhan Mattela:
The Ramifications of Making Deep Neural Networks Compact. CoRR abs/2006.15098 (2020) - [i19]Nandan Kumar Jha, Rajat Saini, Subhrajit Nag, Sparsh Mittal:
E2GC: Energy-efficient Group Convolution in Deep Neural Networks. CoRR abs/2006.15100 (2020) - [i18]Rajat Saini, Nandan Kumar Jha, Bedanta Das, Sparsh Mittal, C. Krishna Mohan:
ULSAM: Ultra-Lightweight Subspace Attention Module for Compact Convolutional Neural Networks. CoRR abs/2006.15102 (2020) - [i17]Nandan Kumar Jha, Shreyas Ravishankar, Sparsh Mittal, Arvind Kaushik, Dipan Mandal, Mahesh Chandra:
DRACO: Co-Optimizing Hardware Utilization, and Performance of DNNs on Systolic Accelerator. CoRR abs/2006.15103 (2020) - [i16]Nandan Kumar Jha, Rajat Saini, Sparsh Mittal:
On the Demystification of Knowledge Distillation: A Residual Network Perspective. CoRR abs/2006.16589 (2020) - [i15]Nandan Kumar Jha, Sparsh Mittal, Binod Kumar, Govardhan Mattela:
DeepPeep: Exploiting Design Ramifications to Decipher the Architecture of Compact DNNs. CoRR abs/2007.15248 (2020) - [i14]Nandan Kumar Jha, Sparsh Mittal:
Modeling Data Reuse in Deep Neural Networks by Taking Data-Types into Cognizance. CoRR abs/2008.02565 (2020)
2010 – 2019
- 2019
- [j49]Sparsh Mittal:
A survey of techniques for dynamic branch prediction. Concurr. Comput. Pract. Exp. 31(1) (2019) - [j48]Sparsh Mittal, Venkat Mattela:
A survey of techniques for improving efficiency of mobile web browsing. Concurr. Comput. Pract. Exp. 31(15) (2019) - [j47]Sumanth Umesh, Sparsh Mittal:
A survey of spintronic architectures for processing-in-memory and neural networks. J. Syst. Archit. 97: 349-372 (2019) - [j46]Sparsh Mittal, Subhrajit Nag:
A survey of encoding techniques for reducing data-movement energy. J. Syst. Archit. 97: 373-396 (2019) - [j45]Sparsh Mittal:
A Survey on optimized implementation of deep learning models on the NVIDIA Jetson platform. J. Syst. Archit. 97: 428-442 (2019) - [j44]Sparsh Mittal:
A survey on applications and architectural-optimizations of Micron's Automata Processor. J. Syst. Archit. 98: 135-164 (2019) - [j43]Sparsh Mittal, Shraiysh Vaishay:
A survey of techniques for optimizing deep learning on GPUs. J. Syst. Archit. 99 (2019) - [j42]Sparsh Mittal:
A Survey of ReRAM-Based Architectures for Processing-In-Memory and Neural Networks. Mach. Learn. Knowl. Extr. 1(1): 75-114 (2019) - [c17]Haonan Wang, Mohamed Assem Ibrahim, Sparsh Mittal, Adwait Jog:
Address-stride assisted approximate load value prediction in GPUs. ICS 2019: 184-194 - [c16]Nandan Kumar Jha, Sparsh Mittal, Govardhan Mattela:
The Ramifications of Making Deep Neural Networks Compact. VLSID 2019: 215-220 - 2018
- [j41]Ahmed Izzat Alsalibi, Sparsh Mittal, Mohammed Azmi Al-Betar, Putra Bin Sumari:
A survey of techniques for architecting SLC/MLC/TLC hybrid Flash memory-based SSDs. Concurr. Comput. Pract. Exp. 30(13) (2018) - [j40]Sparsh Mittal, Ahmed Izzat Alsalibi:
A Survey of Techniques for Improving Security of Non-volatile Memories. J. Hardw. Syst. Secur. 2(2): 179-200 (2018) - [j39]Sparsh Mittal, S. B. Abhinaya, Manish Reddy, Irfan Ali:
A Survey of Techniques for Improving Security of GPUs. J. Hardw. Syst. Secur. 2(3): 266-285 (2018) - [j38]Sparsh Mittal, Maruthi Seshidhar Inukonda:
A survey of techniques for improving error-resilience of DRAM. J. Syst. Archit. 91: 11-40 (2018) - [i13]Sparsh Mittal, S. B. Abhinaya, Manish Reddy, Irfan Ali:
A Survey of Techniques for Improving Security of GPUs. CoRR abs/1804.00114 (2018) - [i12]Sparsh Mittal:
A Survey of Techniques for Dynamic Branch Prediction. CoRR abs/1804.00261 (2018) - 2017
- [j37]Guoliang Zhu, Kai Lu, Xiaoping Wang, Yiming Zhang, Pengfei Zhang, Sparsh Mittal:
SwapX: An NVM-Based Hierarchical Swapping Framework. IEEE Access 5: 16383-16392 (2017) - [j36]Sparsh Mittal, Jeffrey S. Vetter, Lei Jiang:
Addressing Read-Disturbance Issue in STT-RAM by Data Compression and Selective Duplication. IEEE Comput. Archit. Lett. 16(2): 94-98 (2017) - [j35]Rujia Wang, Sparsh Mittal, Youtao Zhang, Jun Yang:
Decongest: Accelerating Super-Dense PCM Under Write Disturbance by Hot Page Remapping. IEEE Comput. Archit. Lett. 16(2): 107-110 (2017) - [j34]Sparsh Mittal:
A Survey of Soft-Error Mitigation Techniques for Non-Volatile Memories. Comput. 6(1): 8 (2017) - [j33]Sparsh Mittal:
A survey of techniques for designing and managing CPU register file. Concurr. Comput. Pract. Exp. 29(4) (2017) - [j32]Sparsh Mittal:
A survey of techniques for architecting TLBs. Concurr. Comput. Pract. Exp. 29(10) (2017) - [j31]Sparsh Mittal:
A survey of value prediction techniques for leveraging value locality. Concurr. Comput. Pract. Exp. 29(21) (2017) - [j30]Sparsh Mittal:
A Survey of Techniques for Cache Partitioning in Multicore Processors. ACM Comput. Surv. 50(2): 27:1-27:39 (2017) - [j29]Sparsh Mittal:
A Survey of Techniques for Architecting and Managing GPU Register File. IEEE Trans. Parallel Distributed Syst. 28(1): 16-28 (2017) - [c15]Lei Jiang, Sparsh Mittal, Wujie Wen:
Building a Fast and Power Efficient Inductive Charge Pump System for 3D Stacked Phase Change Memories. ACM Great Lakes Symposium on VLSI 2017: 275-280 - [c14]Sparsh Mittal, Rajendra Bishnoi, Fabian Oboril, Haonan Wang, Mehdi Baradaran Tahoori, Adwait Jog, Jeffrey S. Vetter:
Architecting SOT-RAM Based GPU Register File. ISVLSI 2017: 38-44 - [c13]Sparsh Mittal, Haonan Wang, Adwait Jog, Jeffrey S. Vetter:
Design and Analysis of Soft-Error Resilience Mechanisms for GPU Register File. VLSID 2017: 409-414 - [i11]Sparsh Mittal:
Mitigating Read-disturbance Errors in STT-RAM Caches by Using Data Compression. CoRR abs/1711.06790 (2017) - 2016
- [j28]Sparsh Mittal:
A Survey of Techniques for Architecting and Managing Asymmetric Multicore Processors. ACM Comput. Surv. 48(3): 45:1-45:38 (2016) - [j27]Sparsh Mittal:
A Survey of Architectural Techniques for Managing Process Variation. ACM Comput. Surv. 48(4): 54:1-54:29 (2016) - [j26]Sparsh Mittal:
A Survey of Techniques for Approximate Computing. ACM Comput. Surv. 48(4): 62:1-62:33 (2016) - [j25]Sparsh Mittal:
A Survey of Recent Prefetching Techniques for Processor Caches. ACM Comput. Surv. 49(2): 35:1-35:35 (2016) - [j24]Sparsh Mittal:
A survey of power management techniques for phase change memory. Int. J. Comput. Aided Eng. Technol. 8(4): 424-444 (2016) - [j23]Sparsh Mittal, Jeffrey S. Vetter:
Reliability Tradeoffs in Design of Volatile and Nonvolatile Caches. J. Circuits Syst. Comput. 25(11): 1650139:1-1650139:14 (2016) - [j22]Sparsh Mittal:
A Survey of Architectural Techniques for Near-Threshold Computing. ACM J. Emerg. Technol. Comput. Syst. 12(4): 46:1-46:26 (2016) - [j21]Sparsh Mittal:
A Survey of Techniques for Architecting Processor Components Using Domain-Wall Memory. ACM J. Emerg. Technol. Comput. Syst. 13(2): 29:1-29:25 (2016) - [j20]Sparsh Mittal:
A Survey of Techniques for Cache Locking. ACM Trans. Design Autom. Electr. Syst. 21(3): 49:1-49:24 (2016) - [j19]Sparsh Mittal, Jeffrey S. Vetter:
A Survey of Techniques for Modeling and Improving Reliability of Computing Systems. IEEE Trans. Parallel Distributed Syst. 27(4): 1226-1238 (2016) - [j18]