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Hadi Esmaeilzadeh
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2020 – today
- 2025
- [j24]Hadi Esmaeilzadeh
, Soroush Ghodrati
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
In-Storage Domain-Specific Acceleration for Serverless Computing. ASPLOS (2) 2024: 530-548 - [c54]Soroush Ghodrati
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
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
Yin-Yang: Programming Abstractions for Cross-Domain Multi-Acceleration. IEEE Micro 42(5): 89-98 (2022) - [j21]Mohammad Loni
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
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
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
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
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
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
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
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
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
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
SiMul: An Algorithm-Driven Approximate Multiplier Design for Machine Learning. IEEE Micro 38(4): 50-59 (2018) - [j17]Divya Mahajan
In-RDBMS Hardware Acceleration of Advanced Analytics. Proc. VLDB Endow. 11(11): 1317-1331 (2018) - [c37]Amir Yazdanbakhsh
In-DRAM near-data approximate acceleration for GPUs. PACT 2018: 34:1-34:14 - [c36]Amir Yazdanbakhsh
FlexiGAN: An End-to-End Solution for FPGA Acceleration of Generative Adversarial Networks. FCCM 2018: 65-72 - [c35]Jacob Sacks, Divya Mahajan
RoboX: An End-to-End Solution to Accelerate Autonomous Control in Robotics. ISCA 2018: 479-490 - [c34]Amir Yazdanbakhsh
GANAX: A Unified MIMD-SIMD Acceleration for Generative Adversarial Networks. ISCA 2018: 650-661 - [c33]Vahideh Akhlaghi, Amir Yazdanbakhsh
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
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
AxBench: A Multiplatform Benchmark Suite for Approximate Computing. IEEE Des. Test 34(2): 60-68 (2017) - [c30]Hyoukjun Kwon
Proving Flow Security of Sequential Logic via Automatically-Synthesized Relational Invariants. CSF 2017: 420-435 - [c29]Jongse Park, Hardik Sharma
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
A reconfigurable fabric for accelerating large-scale datacenter services. Commun. ACM 59(11): 114-122 (2016) - [j14]Amir Yazdanbakhsh
Mitigating the Memory Bottleneck With Approximate Load Value Prediction. IEEE Des. Test 33(1): 32-42 (2016) - [j13]Amir Yazdanbakhsh
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
Error correction for approximate computing. Allerton 2016: 948-953 - [c26]Jongse Park, Emmanuel Amaro, Divya Mahajan
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
TABLA: A unified template-based framework for accelerating statistical machine learning. HPCA 2016: 14-26 - [c23]Divya Mahajan
Towards Statistical Guarantees in Controlling Quality Tradeoffs for Approximate Acceleration. ISCA 2016: 66-77 - [c22]Hardik Sharma, Jongse Park, Divya Mahajan
From high-level deep neural models to FPGAs. MICRO 2016: 17:1-17:12 - 2015
- [j12]Hadi Esmaeilzadeh, Adrian Sampson
Neural acceleration for general-purpose approximate programs. Commun. ACM 58(1): 105-115 (2015) - [j11]Andrew Putnam
A Reconfigurable Fabric for Accelerating Large-Scale Datacenter Services. IEEE Micro 35(3): 10-22 (2015) - [j10]Divya Mahajan
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
SNNAP: Approximate computing on programmable SoCs via neural acceleration. HPCA 2015: 603-614 - [c18]Amir Yazdanbakhsh
Neural acceleration for GPU throughput processors. MICRO 2015: 482-493 - [c17]Jongse Park, Hadi Esmaeilzadeh, Xin Zhang
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
Rollback-free value prediction with approximate loads. PACT 2014: 493-494 - [c15]Andrew Putnam
A reconfigurable fabric for accelerating large-scale datacenter services. ISCA 2014: 13-24 - [c14]Renée St. Amant, Amir Yazdanbakhsh
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
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
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
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
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
Architecture support for disciplined approximate programming. ASPLOS 2012: 301-312 - [c11]Hadi Esmaeilzadeh, Adrian Sampson
Neural Acceleration for General-Purpose Approximate Programs. MICRO 2012: 449-460 - 2011
- [c10]Hadi Esmaeilzadeh, Ting Cao, Xi Yang, Stephen M. Blackburn
Looking back on the language and hardware revolutions: measured power, performance, and scaling. ASPLOS 2011: 319-332 - [c9]Hadi Esmaeilzadeh
Dark silicon and the end of multicore scaling. ISCA 2011: 365-376
2000 – 2009
- 2006
- [j2]Saeed Safari
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
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
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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