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2020 – today
- 2025
[j24]Hadi Esmaeilzadeh
, Soroush Ghodrati, Andrew B. Kahng, Sean Kinzer, Susmita Dey Manasi, Sachin S. Sapatnekar, Zhiang Wang:
Performance Analysis of CNN Inference/Training with Convolution and Non-Convolution Operations on ASIC Accelerators. ACM Trans. Design Autom. Electr. Syst. 30(1): 1-34 (2025)- 2024
- [j23]Hadi Esmaeilzadeh, Soroush Ghodrati, Andrew B. Kahng, Joon Kyung Kim, Sean Kinzer, Sayak Kundu, Rohan Mahapatra, Susmita Dey Manasi, Sachin S. Sapatnekar, Zhiang Wang, Ziqing Zeng:
An Open-Source ML-Based Full-Stack Optimization Framework for Machine Learning Accelerators. ACM Trans. Design Autom. Electr. Syst. 29(4): 1-33 (2024) - [c55]Rohan Mahapatra, Soroush Ghodrati, Byung Hoon Ahn, Sean Kinzer, Shu-Ting Wang, Hanyang Xu, Lavanya Karthikeyan, Hardik Sharma, Amir Yazdanbakhsh, Mohammad Alian, Hadi Esmaeilzadeh:
In-Storage Domain-Specific Acceleration for Serverless Computing. ASPLOS (2) 2024: 530-548 - [c54]Soroush Ghodrati, Sean Kinzer, Hanyang Xu, Rohan Mahapatra, Yoonsung Kim, Byung Hoon Ahn, Dong Kai Wang, Lavanya Karthikeyan, Amir Yazdanbakhsh, Jongse Park, Nam Sung Kim, Hadi Esmaeilzadeh:
Tandem Processor: Grappling with Emerging Operators in Neural Networks. ASPLOS (2) 2024: 1165-1182 - [c53]Shu-Ting Wang, Hanyang Xu, Amin Mamandipoor, Rohan Mahapatra, Byung Hoon Ahn, Soroush Ghodrati, Krishnan Kailas, Mohammad Alian, Hadi Esmaeilzadeh:
Data Motion Acceleration: Chaining Cross-Domain Multi Accelerators. HPCA 2024: 1043-1062 - 2023
- [c52]Dong Kai Wang, Jiaqi Lou, Naiyin Jin, Edwin Mascarenhas, Rohan Mahapatra, Sean Kinzer, Soroush Ghodrati, Amir Yazdanbakhsh, Hadi Esmaeilzadeh, Nam Sung Kim:
MESA: Microarchitecture Extensions for Spatial Architecture Generation. ISCA 2023: 49:1-49:14 - [i21]Rohan Mahapatra, Soroush Ghodrati, Byung Hoon Ahn, Sean Kinzer, Shu-Ting Wang, Hanyang Xu, Lavanya Karthikeyan, Hardik Sharma, Amir Yazdanbakhsh, Mohammad Alian, Hadi Esmaeilzadeh:
Domain-Specific Computational Storage for Serverless Computing. CoRR abs/2303.03483 (2023) - [i20]Hadi Esmaeilzadeh, Soroush Ghodrati, Andrew B. Kahng, Sean Kinzer, Susmita Dey Manasi, Sachin S. Sapatnekar, Zhiang Wang:
Performance Analysis of DNN Inference/Training with Convolution and non-Convolution Operations. CoRR abs/2306.16767 (2023) - [i19]Hadi Esmaeilzadeh, Soroush Ghodrati, Andrew B. Kahng, Joon Kyung Kim, Sean Kinzer, Sayak Kundu, Rohan Mahapatra, Susmita Dey Manasi, Sachin S. Sapatnekar, Zhiang Wang, Ziqing Zeng:
An Open-Source ML-Based Full-Stack Optimization Framework for Machine Learning Accelerators. CoRR abs/2308.12120 (2023) - [i18]Sean Kinzer, Soroush Ghodrati, Rohan Mahapatra, Byung Hoon Ahn, Edwin Mascarenhas, Xiaolong Li, Janarbek Matai, Liang Zhang, Hadi Esmaeilzadeh:
Restoring the Broken Covenant Between Compilers and Deep Learning Accelerators. CoRR abs/2310.17912 (2023) - 2022
- [j22]Joon Kyung Kim, Byung Hoon Ahn, Sean Kinzer, Soroush Ghodrati, Rohan Mahapatra, Brahmendra Reddy Yatham, Shu-Ting Wang, Dohee Kim, Parisa Sarikhani, Babak Mahmoudi, Divya Mahajan, Jongse Park, Hadi Esmaeilzadeh:
Yin-Yang: Programming Abstractions for Cross-Domain Multi-Acceleration. IEEE Micro 42(5): 89-98 (2022) - [j21]Mohammad Loni, Ali Zoljodi, Amin Majd, Byung Hoon Ahn, Masoud Daneshtalab, Mikael Sjödin, Hadi Esmaeilzadeh:
FastStereoNet: A Fast Neural Architecture Search for Improving the Inference of Disparity Estimation on Resource-Limited Platforms. IEEE Trans. Syst. Man Cybern. Syst. 52(8): 5222-5234 (2022) - [c51]Byung Hoon Ahn, Sean Kinzer, Hadi Esmaeilzadeh:
Glimpse: mathematical embedding of hardware specification for neural compilation. DAC 2022: 1165-1170 - [c50]Zheng Li, Soroush Ghodrati, Amir Yazdanbakhsh, Hadi Esmaeilzadeh, Mingu Kang:
Accelerating attention through gradient-based learned runtime pruning. ISCA 2022: 902-915 - [c49]Hadi Esmaeilzadeh, Soroush Ghodrati, Andrew B. Kahng, Joon Kyung Kim, Sean Kinzer, Sayak Kundu, Rohan Mahapatra, Susmita Dey Manasi, Sachin S. Sapatnekar, Zhiang Wang, Ziqing Zeng:
Physically Accurate Learning-based Performance Prediction of Hardware-accelerated ML Algorithms. MLCAD 2022: 119-126 - [i17]Zheng Li, Soroush Ghodrati, Amir Yazdanbakhsh, Hadi Esmaeilzadeh, Mingu Kang:
Accelerating Attention through Gradient-Based Learned Runtime Pruning. CoRR abs/2204.03227 (2022) - 2021
- [c48]Sean Kinzer, Joon Kyung Kim, Soroush Ghodrati, Brahmendra Reddy Yatham, Alric Althoff, Divya Mahajan, Sorin Lerner, Hadi Esmaeilzadeh:
A Computational Stack for Cross-Domain Acceleration. HPCA 2021: 54-70 - [c47]Hadi Esmaeilzadeh, Soroush Ghodrati, Jie Gu, Shiyu Guo, Andrew B. Kahng, Joon Kyung Kim, Sean Kinzer, Rohan Mahapatra, Susmita Dey Manasi, Edwin Mascarenhas, Sachin S. Sapatnekar, Ravi Varadarajan, Zhiang Wang, Hanyang Xu, Brahmendra Reddy Yatham, Ziqing Zeng:
VeriGOOD-ML: An Open-Source Flow for Automated ML Hardware Synthesis. ICCAD 2021: 1-7 - [c46]Fatemehsadat Mireshghallah, Mohammadkazem Taram, Ali Jalali, Ahmed Taha Elthakeb, Dean M. Tullsen, Hadi Esmaeilzadeh:
Not All Features Are Equal: Discovering Essential Features for Preserving Prediction Privacy. WWW 2021: 669-680 - [i16]Hadi Esmaeilzadeh, Reza Vaezi:
Conscious AI. CoRR abs/2105.07879 (2021) - 2020
- [j20]Ahmed T. Elthakeb, Prannoy Pilligundla, Fatemehsadat Mireshghallah, Amir Yazdanbakhsh, Hadi Esmaeilzadeh:
ReLeQ : A Reinforcement Learning Approach for Automatic Deep Quantization of Neural Networks. IEEE Micro 40(5): 37-45 (2020) - [c45]Soroush Ghodrati, Hardik Sharma, Sean Kinzer, Amir Yazdanbakhsh, Jongse Park, Nam Sung Kim, Doug Burger, Hadi Esmaeilzadeh:
Mixed-Signal Charge-Domain Acceleration of Deep Neural Networks through Interleaved Bit-Partitioned Arithmetic. PACT 2020: 399-411 - [c44]Fatemehsadat Mireshghallah, Mohammadkazem Taram, Prakash Ramrakhyani, Ali Jalali, Dean M. Tullsen, Hadi Esmaeilzadeh:
Shredder: Learning Noise Distributions to Protect Inference Privacy. ASPLOS 2020: 3-18 - [c43]Soroush Ghodrati, Hardik Sharma, Cliff Young, Nam Sung Kim, Hadi Esmaeilzadeh:
Bit-Parallel Vector Composability for Neural Acceleration. DAC 2020: 1-6 - [c42]Byung Hoon Ahn, Prannoy Pilligundla, Amir Yazdanbakhsh, Hadi Esmaeilzadeh:
Chameleon: Adaptive Code Optimization for Expedited Deep Neural Network Compilation. ICLR 2020 - [c41]Ahmed Taha Elthakeb, Prannoy Pilligundla, Fatemeh Mireshghallah, Alexander Cloninger, Hadi Esmaeilzadeh:
Divide and Conquer: Leveraging Intermediate Feature Representations for Quantized Training of Neural Networks. ICML 2020: 2880-2891 - [c40]Soroush Ghodrati, Byung Hoon Ahn, Joon Kyung Kim, Sean Kinzer, Brahmendra Reddy Yatham, Navateja Alla, Hardik Sharma, Mohammad Alian, Eiman Ebrahimi, Nam Sung Kim, Cliff Young, Hadi Esmaeilzadeh:
Planaria: Dynamic Architecture Fission for Spatial Multi-Tenant Acceleration of Deep Neural Networks. MICRO 2020: 681-697 - [c39]Byung Hoon Ahn, Jinwon Lee, Jamie Menjay Lin, Hsin-Pai Cheng, Jilei Hou, Hadi Esmaeilzadeh:
Ordering Chaos: Memory-Aware Scheduling of Irregularly Wired Neural Networks for Edge Devices. MLSys 2020 - [i15]Byung Hoon Ahn, Prannoy Pilligundla, Amir Yazdanbakhsh, Hadi Esmaeilzadeh:
Chameleon: Adaptive Code Optimization for Expedited Deep Neural Network Compilation. CoRR abs/2001.08743 (2020) - [i14]Ahmed T. Elthakeb, Prannoy Pilligundla, Fatemehsadat Mireshghallah, Tarek Elgindi, Charles-Alban Deledalle, Hadi Esmaeilzadeh:
Gradient-Based Deep Quantization of Neural Networks through Sinusoidal Adaptive Regularization. CoRR abs/2003.00146 (2020) - [i13]Byung Hoon Ahn, Jinwon Lee, Jamie Menjay Lin, Hsin-Pai Cheng, Jilei Hou, Hadi Esmaeilzadeh:
Ordering Chaos: Memory-Aware Scheduling of Irregularly Wired Neural Networks for Edge Devices. CoRR abs/2003.02369 (2020) - [i12]Fatemehsadat Mireshghallah, Mohammadkazem Taram, Ali Jalali, Ahmed Taha Elthakeb, Dean M. Tullsen, Hadi Esmaeilzadeh:
A Principled Approach to Learning Stochastic Representations for Privacy in Deep Neural Inference. CoRR abs/2003.12154 (2020) - [i11]Soroush Ghodrati, Hardik Sharma, Cliff Young, Nam Sung Kim, Hadi Esmaeilzadeh:
Bit-Parallel Vector Composability for Neural Acceleration. CoRR abs/2004.05333 (2020) - [i10]Fatemehsadat Mireshghallah, Mohammadkazem Taram, Praneeth Vepakomma, Abhishek Singh, Ramesh Raskar, Hadi Esmaeilzadeh:
Privacy in Deep Learning: A Survey. CoRR abs/2004.12254 (2020)
2010 – 2019
- 2019
- [j19]Hadi Esmaeilzadeh, Jongse Park:
Machine Learning Acceleration. IEEE Micro 39(5): 6-7 (2019) - [c38]Zhenhong Liu, Amir Yazdanbakhsh, Dong Kai Wang, Hadi Esmaeilzadeh, Nam Sung Kim:
AxMemo: hardware-compiler co-design for approximate code memoization. ISCA 2019: 685-697 - [p1]Amir Yazdanbakhsh, Gennady Pekhimenko, Hadi Esmaeilzadeh, Onur Mutlu, Todd C. Mowry:
Towards Breaking the Memory Bandwidth Wall Using Approximate Value Prediction. Approximate Circuits 2019: 417-441 - [i9]Ahmed T. Elthakeb, Prannoy Pilligundla, Hadi Esmaeilzadeh:
SinReQ: Generalized Sinusoidal Regularization for Automatic Low-Bitwidth Deep Quantized Training. CoRR abs/1905.01416 (2019) - [i8]Fatemehsadat Mireshghallah, Mohammadkazem Taram, Prakash Ramrakhyani, Dean M. Tullsen, Hadi Esmaeilzadeh:
Shredder: Learning Noise to Protect Privacy with Partial DNN Inference on the Edge. CoRR abs/1905.11814 (2019) - [i7]Byung Hoon Ahn, Prannoy Pilligundla, Hadi Esmaeilzadeh:
Reinforcement Learning and Adaptive Sampling for Optimized DNN Compilation. CoRR abs/1905.12799 (2019) - [i6]Ahmed T. Elthakeb, Prannoy Pilligundla, Hadi Esmaeilzadeh:
Divide and Conquer: Leveraging Intermediate Feature Representations for Quantized Training of Neural Networks. CoRR abs/1906.06033 (2019) - [i5]Soroush Ghodrati, Hardik Sharma, Sean Kinzer, Amir Yazdanbakhsh, Kambiz Samadi, Nam Sung Kim, Doug Burger, Hadi Esmaeilzadeh:
Mixed-Signal Charge-Domain Acceleration of Deep Neural networks through Interleaved Bit-Partitioned Arithmetic. CoRR abs/1906.11915 (2019) - 2018
- [j18]Zhenhong Liu, Amir Yazdanbakhsh, Taejoon Park, Hadi Esmaeilzadeh, Nam Sung Kim:
SiMul: An Algorithm-Driven Approximate Multiplier Design for Machine Learning. IEEE Micro 38(4): 50-59 (2018) - [j17]Divya Mahajan, Joon Kyung Kim, Jacob Sacks, Adel Ardalan, Arun Kumar, Hadi Esmaeilzadeh:
In-RDBMS Hardware Acceleration of Advanced Analytics. Proc. VLDB Endow. 11(11): 1317-1331 (2018) - [c37]Amir Yazdanbakhsh, Choungki Song, Jacob Sacks, Pejman Lotfi-Kamran, Hadi Esmaeilzadeh, Nam Sung Kim:
In-DRAM near-data approximate acceleration for GPUs. PACT 2018: 34:1-34:14 - [c36]Amir Yazdanbakhsh, Michael Brzozowski, Behnam Khaleghi, Soroush Ghodrati, Kambiz Samadi, Nam Sung Kim, Hadi Esmaeilzadeh:
FlexiGAN: An End-to-End Solution for FPGA Acceleration of Generative Adversarial Networks. FCCM 2018: 65-72 - [c35]Jacob Sacks, Divya Mahajan, Richard Connor Lawson, Hadi Esmaeilzadeh:
RoboX: An End-to-End Solution to Accelerate Autonomous Control in Robotics. ISCA 2018: 479-490 - [c34]Amir Yazdanbakhsh, Kambiz Samadi, Nam Sung Kim, Hadi Esmaeilzadeh:
GANAX: A Unified MIMD-SIMD Acceleration for Generative Adversarial Networks. ISCA 2018: 650-661 - [c33]Vahideh Akhlaghi, Amir Yazdanbakhsh, Kambiz Samadi, Rajesh K. Gupta, Hadi Esmaeilzadeh:
SnaPEA: Predictive Early Activation for Reducing Computation in Deep Convolutional Neural Networks. ISCA 2018: 662-673 - [c32]Hardik Sharma, Jongse Park, Naveen Suda, Liangzhen Lai, Benson Chau, Vikas Chandra, Hadi Esmaeilzadeh:
Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Network. ISCA 2018: 764-775 - [c31]Youjie Li, Jongse Park, Mohammad Alian, Yifan Yuan, Zheng Qu, Peitian Pan, Ren Wang, Alexander G. Schwing, Hadi Esmaeilzadeh, Nam Sung Kim:
A Network-Centric Hardware/Algorithm Co-Design to Accelerate Distributed Training of Deep Neural Networks. MICRO 2018: 175-188 - [i4]Divya Mahajan, Joon Kyung Kim, Jacob Sacks, Adel Ardalan, Arun Kumar, Hadi Esmaeilzadeh:
In-RDBMS Hardware Acceleration of Advanced Analytics. CoRR abs/1801.06027 (2018) - [i3]Amir Yazdanbakhsh, Hajar Falahati, Philip J. Wolfe, Kambiz Samadi, Nam Sung Kim, Hadi Esmaeilzadeh:
GANAX: A Unified MIMD-SIMD Acceleration for Generative Adversarial Networks. CoRR abs/1806.01107 (2018) - [i2]Ahmed T. Elthakeb, Prannoy Pilligundla, Amir Yazdanbakhsh, Sean Kinzer, Hadi Esmaeilzadeh:
ReLeQ: A Reinforcement Learning Approach for Deep Quantization of Neural Networks. CoRR abs/1811.01704 (2018) - 2017
- [j16]Amir Yazdanbakhsh, Divya Mahajan, Hadi Esmaeilzadeh, Pejman Lotfi-Kamran:
AxBench: A Multiplatform Benchmark Suite for Approximate Computing. IEEE Des. Test 34(2): 60-68 (2017) - [c30]Hyoukjun Kwon, William Harris, Hadi Esmaeilzadeh:
Proving Flow Security of Sequential Logic via Automatically-Synthesized Relational Invariants. CSF 2017: 420-435 - [c29]Jongse Park, Hardik Sharma, Divya Mahajan, Joon Kyung Kim, Preston Olds, Hadi Esmaeilzadeh:
Scale-out acceleration for machine learning. MICRO 2017: 367-381 - [i1]Hardik Sharma, Jongse Park, Naveen Suda, Liangzhen Lai, Benson Chau, Joon Kyung Kim, Vikas Chandra, Hadi Esmaeilzadeh:
Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Networks. CoRR abs/1712.01507 (2017) - 2016
- [j15]Andrew Putnam, Adrian M. Caulfield, Eric S. Chung, Derek Chiou, Kypros Constantinides, John Demme, Hadi Esmaeilzadeh, Jeremy Fowers, Gopi Prashanth Gopal, Jan Gray, Michael Haselman, Scott Hauck, Stephen Heil, Amir Hormati, Joo-Young Kim, Sitaram Lanka, James R. Larus, Eric Peterson, Simon Pope, Aaron Smith, Jason Thong, Phillip Yi Xiao, Doug Burger:
A reconfigurable fabric for accelerating large-scale datacenter services. Commun. ACM 59(11): 114-122 (2016) - [j14]Amir Yazdanbakhsh, Bradley Thwaites, Hadi Esmaeilzadeh, Gennady Pekhimenko, Onur Mutlu, Todd C. Mowry:
Mitigating the Memory Bottleneck With Approximate Load Value Prediction. IEEE Des. Test 33(1): 32-42 (2016) - [j13]Amir Yazdanbakhsh, Gennady Pekhimenko, Bradley Thwaites, Hadi Esmaeilzadeh, Onur Mutlu, Todd C. Mowry:
RFVP: Rollback-Free Value Prediction with Safe-to-Approximate Loads. ACM Trans. Archit. Code Optim. 12(4): 62:1-62:26 (2016) - [c28]William Wahby, Thomas E. Sarvey, Hardik Sharma, Hadi Esmaeilzadeh, Muhannad S. Bakir:
The impact of 3D stacking on GPU-accelerated deep neural networks: An experimental study. 3DIC 2016: 1-4 - [c27]Hang Zhang, Afshin Abdi, Faramarz Fekri, Hadi Esmaeilzadeh:
Error correction for approximate computing. Allerton 2016: 948-953 - [c26]Jongse Park, Emmanuel Amaro, Divya Mahajan, Bradley Thwaites, Hadi Esmaeilzadeh:
AxGames: Towards Crowdsourcing Quality Target Determination in Approximate Computing. ASPLOS 2016: 623-636 - [c25]Atieh Lotfi, Abbas Rahimi, Amir Yazdanbakhsh, Hadi Esmaeilzadeh, Rajesh K. Gupta:
Grater: An approximation workflow for exploiting data-level parallelism in FPGA acceleration. DATE 2016: 1279-1284 - [c24]Divya Mahajan, Jongse Park, Emmanuel Amaro, Hardik Sharma, Amir Yazdanbakhsh, Joon Kyung Kim, Hadi Esmaeilzadeh:
TABLA: A unified template-based framework for accelerating statistical machine learning. HPCA 2016: 14-26 - [c23]Divya Mahajan, Amir Yazdanbakhsh, Jongse Park, Bradley Thwaites, Hadi Esmaeilzadeh:
Towards Statistical Guarantees in Controlling Quality Tradeoffs for Approximate Acceleration. ISCA 2016: 66-77 - [c22]Hardik Sharma, Jongse Park, Divya Mahajan, Emmanuel Amaro, Joon Kyung Kim, Chenkai Shao, Asit Mishra, Hadi Esmaeilzadeh:
From high-level deep neural models to FPGAs. MICRO 2016: 17:1-17:12 - 2015
- [j12]Hadi Esmaeilzadeh, Adrian Sampson, Luis Ceze, Doug Burger:
Neural acceleration for general-purpose approximate programs. Commun. ACM 58(1): 105-115 (2015) - [j11]Andrew Putnam, Adrian M. Caulfield, Eric S. Chung, Derek Chiou, Kypros Constantinides, John Demme, Hadi Esmaeilzadeh, Jeremy Fowers, Gopi Prashanth Gopal, Jan Gray, Michael Haselman, Scott Hauck, Stephen Heil, Amir Hormati, Joo-Young Kim, Sitaram Lanka, James R. Larus, Eric Peterson, Simon Pope, Aaron Smith, Jason Thong, Phillip Yi Xiao, Doug Burger:
A Reconfigurable Fabric for Accelerating Large-Scale Datacenter Services. IEEE Micro 35(3): 10-22 (2015) - [j10]Divya Mahajan, Kartik Ramkrishnan, Rudra Jariwala, Amir Yazdanbakhsh, Jongse Park, Bradley Thwaites, Anandhavel Nagendrakumar, Abbas Rahimi, Hadi Esmaeilzadeh, Kia Bazargan:
Axilog: Abstractions for Approximate Hardware Design and Reuse. IEEE Micro 35(5): 16-30 (2015) - [c21]Hadi Esmaeilzadeh:
Approximate acceleration: A path through the era of dark silicon and big data. CASES 2015: 31-32 - [c20]Amir Yazdanbakhsh, Divya Mahajan, Bradley Thwaites, Jongse Park, Anandhavel Nagendrakumar, Sindhuja Sethuraman, Kartik Ramkrishnan, Nishanthi Ravindran, Rudra Jariwala, Abbas Rahimi, Hadi Esmaeilzadeh, Kia Bazargan:
Axilog: language support for approximate hardware design. DATE 2015: 812-817 - [c19]Thierry Moreau, Mark Wyse, Jacob Nelson, Adrian Sampson, Hadi Esmaeilzadeh, Luis Ceze, Mark Oskin:
SNNAP: Approximate computing on programmable SoCs via neural acceleration. HPCA 2015: 603-614 - [c18]Amir Yazdanbakhsh, Jongse Park, Hardik Sharma, Pejman Lotfi-Kamran, Hadi Esmaeilzadeh:
Neural acceleration for GPU throughput processors. MICRO 2015: 482-493 - [c17]Jongse Park, Hadi Esmaeilzadeh, Xin Zhang, Mayur Naik, William Harris:
FlexJava: language support for safe and modular approximate programming. ESEC/SIGSOFT FSE 2015: 745-757 - 2014
- [c16]Bradley Thwaites, Gennady Pekhimenko, Hadi Esmaeilzadeh, Amir Yazdanbakhsh, Onur Mutlu, Jongse Park, Girish Mururu, Todd C. Mowry:
Rollback-free value prediction with approximate loads. PACT 2014: 493-494 - [c15]Andrew Putnam, Adrian M. Caulfield, Eric S. Chung, Derek Chiou, Kypros Constantinides, John Demme, Hadi Esmaeilzadeh, Jeremy Fowers, Gopi Prashanth Gopal, Jan Gray, Michael Haselman, Scott Hauck, Stephen Heil, Amir Hormati, Joo-Young Kim, Sitaram Lanka, James R. Larus, Eric Peterson, Simon Pope, Aaron Smith, Jason Thong, Phillip Yi Xiao, Doug Burger:
A reconfigurable fabric for accelerating large-scale datacenter services. ISCA 2014: 13-24 - [c14]Renée St. Amant, Amir Yazdanbakhsh, Jongse Park, Bradley Thwaites, Hadi Esmaeilzadeh, Arjang Hassibi, Luis Ceze, Doug Burger:
General-purpose code acceleration with limited-precision analog computation. ISCA 2014: 505-516 - 2013
- [b1]Hadi Esmaeilzadeh:
Approximate Acceleration for a Post Multicore Era. University of Washington, USA, 2013 - [j9]Hadi Esmaeilzadeh, Emily R. Blem, Renée St. Amant, Karthikeyan Sankaralingam, Doug Burger:
Power challenges may end the multicore era. Commun. ACM 56(2): 93-102 (2013) - [j8]Emily R. Blem, Hadi Esmaeilzadeh, Renée St. Amant, Karthikeyan Sankaralingam, Doug Burger:
Multicore Model from Abstract Single Core Inputs. IEEE Comput. Archit. Lett. 12(2): 59-62 (2013) - [j7]Hadi Esmaeilzadeh, Adrian Sampson, Luis Ceze, Doug Burger:
Neural Acceleration for General-Purpose Approximate Programs. IEEE Micro 33(3): 16-27 (2013) - [c13]Behnam Robatmili, Dong Li, Hadi Esmaeilzadeh, Madhu Saravana Sibi Govindan, Aaron Smith, Andrew Putnam, Doug Burger, Stephen W. Keckler:
How to implement effective prediction and forwarding for fusable dynamic multicore architectures. HPCA 2013: 460-471 - 2012
- [j6]Hadi Esmaeilzadeh, Ting Cao, Xi Yang, Stephen M. Blackburn, Kathryn S. McKinley:
Looking back and looking forward: power, performance, and upheaval. Commun. ACM 55(7): 105-114 (2012) - [j5]Hadi Esmaeilzadeh, Ting Cao, Xi Yang, Stephen M. Blackburn, Kathryn S. McKinley:
What is Happening to Power, Performance, and Software? IEEE Micro 32(3): 110-121 (2012) - [j4]Hadi Esmaeilzadeh, Emily R. Blem, Renée St. Amant, Karthikeyan Sankaralingam, Doug Burger:
Dark Silicon and the End of Multicore Scaling. IEEE Micro 32(3): 122-134 (2012) - [j3]Hadi Esmaeilzadeh, Emily R. Blem, Renée St. Amant, Karthikeyan Sankaralingam, Doug Burger:
Power Limitations and Dark Silicon Challenge the Future of Multicore. ACM Trans. Comput. Syst. 30(3): 11:1-11:27 (2012) - [c12]Hadi Esmaeilzadeh, Adrian Sampson, Luis Ceze, Doug Burger:
Architecture support for disciplined approximate programming. ASPLOS 2012: 301-312 - [c11]Hadi Esmaeilzadeh, Adrian Sampson, Luis Ceze, Doug Burger:
Neural Acceleration for General-Purpose Approximate Programs. MICRO 2012: 449-460 - 2011
- [c10]Hadi Esmaeilzadeh, Ting Cao, Xi Yang, Stephen M. Blackburn, Kathryn S. McKinley:
Looking back on the language and hardware revolutions: measured power, performance, and scaling. ASPLOS 2011: 319-332 - [c9]Hadi Esmaeilzadeh, Emily R. Blem, Renée St. Amant, Karthikeyan Sankaralingam, Doug Burger:
Dark silicon and the end of multicore scaling. ISCA 2011: 365-376
2000 – 2009
- 2006
- [j2]Saeed Safari, Amir-Hossein Jahangir, Hadi Esmaeilzadeh:
A parameterized graph-based framework for high-level test synthesis. Integr. 39(4): 363-381 (2006) - [c8]Hadi Esmaeilzadeh, A. Moghimi, Eiman Ebrahimi, Caro Lucas, Zainalabedin Navabi, A. M. Fakhraie:
DCim++: a C++ library for object oriented hardware design and distributed simulation. ISCAS 2006 - [c7]Hadi Esmaeilzadeh, Pooya Saeedi, Babak Nadjar Araabi, Caro Lucas, Seid Mehdi Fakhraie:
Neural network stream processing core (NnSP) for embedded systems. ISCAS 2006 - 2005
- [j1]Saeed Shamshiri, Hadi Esmaeilzadeh, Zainalabedin Navabi:
Instruction-level test methodology for CPU core self-testing. ACM Trans. Design Autom. Electr. Syst. 10(4): 673-689 (2005) - [c6]Hadi Esmaeilzadeh, Saeed Shamshiri, Pooya Saeedi, Zainalabedin Navabi:
ISC: Reconfigurable Scan-Cell Architecture for Low Power Testing. Asian Test Symposium 2005: 236-241 - 2004
- [c5]Saeed Shamshiri, Hadi Esmaeilzadeh, Zainalabedin Navabi:
Test Instruction Set (TIS) for High Level Self-Testing of CPU Cores. Asian Test Symposium 2004: 158-163 - [c4]Saeed Shamshiri, Hadi Esmaeilzadeh, Zainalabedin Navabi:
Instruction level test methodology for CPU core software-based self-testing. HLDVT 2004: 25-29 - [c3]Neda Shahidi, Hadi Esmaeilzadeh, Marziye Abdollahi, Caro Lucas:
Memetic Algorithm Based Path Planning for a Mobile Robot. International Conference on Computational Intelligence 2004: 56-59 - 2003
- [c2]Saeed Safari, Hadi Esmaeilzadeh, Amir-Hossein Jahangir:
Testability Improvement During High-Level Synthesis. Asian Test Symposium 2003: 505 - [c1]Saeed Safari, Hadi Esmaeilzadeh, Amir-Hossein Jahangir:
A novel improvement technique for high-level test synthesis. ISCAS (5) 2003: 609-612
Coauthor Index
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Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-12-23 19:34 CET by the dblp team
all metadata released as open data under CC0 1.0 license
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since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
