Udit Gupta
Citata da
Citata da
Ares: A framework for quantifying the resilience of deep neural networks
B Reagen, U Gupta, L Pentecost, P Whatmough, SK Lee, N Mulholland, ...
2018 55th ACM/ESDA/IEEE Design Automation Conference (DAC), 1-6, 2018
Deep learning recommendation model for personalization and recommendation systems
M Naumov, D Mudigere, HJM Shi, J Huang, N Sundaraman, J Park, ...
arXiv preprint arXiv:1906.00091, 2019
Mlperf training benchmark
P Mattson, C Cheng, C Coleman, G Diamos, P Micikevicius, D Patterson, ...
arXiv preprint arXiv:1910.01500, 2019
The architectural implications of facebook's dnn-based personalized recommendation
U Gupta, CJ Wu, X Wang, M Naumov, B Reagen, D Brooks, B Cottel, ...
2020 IEEE International Symposium on High Performance Computer Architecture …, 2020
Rosetta: A realistic high-level synthesis benchmark suite for software programmable fpgas
Y Zhou, U Gupta, S Dai, R Zhao, N Srivastava, H Jin, J Featherston, ...
Proceedings of the 2018 ACM/SIGDA International Symposium on Field …, 2018
Dynamic hazard resolution for pipelining irregular loops in high-level synthesis
S Dai, R Zhao, G Liu, S Srinath, U Gupta, C Batten, Z Zhang
Proceedings of the 2017 ACM/SIGDA International Symposium on Field …, 2017
On-chip deep neural network storage with multi-level eNVM
M Donato, B Reagen, L Pentecost, U Gupta, D Brooks, GY Wei
Proceedings of the 55th Annual Design Automation Conference, 1-6, 2018
MASR: A modular accelerator for sparse rnns
U Gupta, B Reagen, L Pentecost, M Donato, T Tambe, AM Rush, GY Wei, ...
2019 28th International Conference on Parallel Architectures and Compilation …, 2019
Weightless: Lossy weight encoding for deep neural network compression
B Reagan, U Gupta, B Adolf, M Mitzenmacher, A Rush, GY Wei, D Brooks
International Conference on Machine Learning, 4324-4333, 2018
DeepRecSys: A System for Optimizing End-To-End At-scale Neural Recommendation Inference
U Gupta, S Hsia, V Saraph, X Wang, B Reagen, GY Wei, HHS Lee, ...
2020 ACM/IEEE 47th Annual International Symposium on Computer Architecture …, 2020
Recnmp: Accelerating personalized recommendation with near-memory processing
L Ke, U Gupta, BY Cho, D Brooks, V Chandra, U Diril, A Firoozshahian, ...
2020 ACM/IEEE 47th Annual International Symposium on Computer Architecture …, 2020
Maxnvm: Maximizing dnn storage density and inference efficiency with sparse encoding and error mitigation
L Pentecost, M Donato, B Reagen, U Gupta, S Ma, GY Wei, D Brooks
Proceedings of the 52Nd Annual IEEE/ACM International Symposium on …, 2019
Mapping-aware constrained scheduling for LUT-based FPGAs
M Tan, S Dai, U Gupta, Z Zhang
Proceedings of the 2015 ACM/SIGDA International Symposium on Field …, 2015
A 16nm 25mm2 SoC with a 54.5x Flexibility-Efficiency Range from Dual-Core Arm Cortex-A53 to eFPGA and Cache-Coherent Accelerators
PN Whatmough, SK Lee, M Donato, HC Hsueh, S Xi, U Gupta, ...
2019 Symposium on VLSI Circuits, C34-C35, 2019
Deep learning: Its not all about recognizing cats and dogs
CJ Wu, D Brooks, U Gupta, HH Lee, K Hazelwood
ACM Sigarch.[Online]. Available: https://www. sigarch. org/deeplearning-its …, 2019
Rosetta: A realistic benchmark suite for software programmable fpgas
U Gupta, S Dai, Z Zhang
Suite of Embedded Applications and Kernels Workshop (SEAK), 2015
Cross-Stack Workload Characterization of Deep Recommendation Systems
S Hsia, U Gupta, M Wilkening, CJ Wu, GY Wei, D Brooks
2020 IEEE International Symposium on Workload Characterization (IISWC), 157-168, 2020
RecSSD: Near Data Processing for Solid State Drive Based Recommendation Inference
M Wilkening, U Gupta, S Hsia, C Trippel, CJ Wu, D Brooks, GY Wei
arXiv preprint arXiv:2102.00075, 2021
Chasing Carbon: The Elusive Environmental Footprint of Computing
U Gupta, YG Kim, S Lee, J Tse, HHS Lee, GY Wei, D Brooks, CJ Wu
arXiv preprint arXiv:2011.02839, 2020
Emerging neural workloads and their impact on hardware
D Brooks, MM Frank, T Gokmen, U Gupta, XS Hu, S Jain, AF Laguna, ...
2020 Design, Automation & Test in Europe Conference & Exhibition (DATE …, 2020
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