Jungwook Choi
Citata da
Citata da
Pact: Parameterized clipping activation for quantized neural networks
J Choi, Z Wang, S Venkataramani, PIJ Chuang, V Srinivasan, ...
arXiv preprint arXiv:1805.06085, 2018
Training deep neural networks with 8-bit floating point numbers
N Wang, J Choi, D Brand, CY Chen, K Gopalakrishnan
Advances in neural information processing systems 31, 2018
Hybrid 8-bit floating point (HFP8) training and inference for deep neural networks
X Sun, J Choi, CY Chen, N Wang, S Venkataramani, VV Srinivasan, X Cui, ...
Advances in neural information processing systems 32, 2019
Accurate and Efficient 2-bit Quantized Neural Networks
J Choi, S Venkataramani, V Srinivasan, K Gopalakrishnan, Z Wang, ...
The Conference on Systems and Machine Learning (SysML), 2019
Adacomp: Adaptive residual gradient compression for data-parallel distributed training
CY Chen, J Choi, D Brand, A Agrawal, W Zhang, K Gopalakrishnan
Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018
A scalable multi-TeraOPS deep learning processor core for AI trainina and inference
B Fleischer, S Shukla, M Ziegler, J Silberman, J Oh, V Srinivasan, J Choi, ...
2018 IEEE symposium on VLSI circuits, 35-36, 2018
Robust machine learning systems: Challenges, current trends, perspectives, and the road ahead
M Shafique, M Naseer, T Theocharides, C Kyrkou, O Mutlu, L Orosa, ...
IEEE Design & Test 37 (2), 30-57, 2020
Approximate computing: Challenges and opportunities
A Agrawal, J Choi, K Gopalakrishnan, S Gupta, R Nair, J Oh, DA Prener, ...
2016 IEEE International Conference on Rebooting Computing (ICRC), 1-8, 2016
DLFloat: A 16-bit Floating Point Format Designed for Deep Learning Training and Inference
A Agrawal, SM Mueller, BM Fleischer, J Choi, N Wang, X Sun, ...
26th IEEE Symposium on Computer Arithmetic, 2019
Bridging the accuracy gap for 2-bit quantized neural networks (qnn)
J Choi, PIJ Chuang, Z Wang, S Venkataramani, V Srinivasan, ...
arXiv preprint arXiv:1807.06964, 2018
Exploiting approximate computing for deep learning acceleration
CY Chen, J Choi, K Gopalakrishnan, V Srinivasan, S Venkataramani
2018 Design, Automation & Test in Europe Conference & Exhibition (DATE), 821-826, 2018
Compensated-DNN: Energy efficient low-precision deep neural networks by compensating quantization errors
S Jain, S Venkataramani, V Srinivasan, J Choi, P Chuang, L Chang
Proceedings of the 55th annual design automation conference, 1-6, 2018
PROMISE: An end-to-end design of a programmable mixed-signal accelerator for machine-learning algorithms
P Srivastava, M Kang, SK Gonugondla, S Lim, J Choi, V Adve, NS Kim, ...
2018 ACM/IEEE 45th Annual International Symposium on Computer Architecture …, 2018
9.1 A 7nm 4-core AI chip with 25.6 TFLOPS hybrid FP8 training, 102.4 TOPS INT4 inference and workload-aware throttling
A Agrawal, SK Lee, J Silberman, M Ziegler, M Kang, S Venkataramani, ...
2021 IEEE International Solid-State Circuits Conference (ISSCC) 64, 144-146, 2021
RaPiD: AI accelerator for ultra-low precision training and inference
S Venkataramani, V Srinivasan, W Wang, S Sen, J Zhang, A Agrawal, ...
2021 ACM/IEEE 48th Annual International Symposium on Computer Architecture …, 2021
A real-time FPGA-based 20 000-word speech recognizer with optimized DRAM access
YK Choi, K You, J Choi, W Sung
IEEE Transactions on Circuits and Systems I: Regular Papers 57 (8), 2119-2131, 2010
Efficient AI system design with cross-layer approximate computing
S Venkataramani, X Sun, N Wang, CY Chen, J Choi, M Kang, A Agarwal, ...
Proceedings of the IEEE 108 (12), 2232-2250, 2020
BiScaled-DNN: Quantizing long-tailed datastructures with two scale factors for deep neural networks
S Jain, S Venkataramani, V Srinivasan, J Choi, K Gopalakrishnan, ...
Proceedings of the 56th Annual Design Automation Conference 2019, 1-6, 2019
A 3.0 TFLOPS 0.62 V scalable processor core for high compute utilization AI training and inference
J Oh, SK Lee, M Kang, M Ziegler, J Silberman, A Agrawal, ...
2020 IEEE Symposium on VLSI Circuits, 1-2, 2020
Accumulation bit-width scaling for ultra-low precision training of deep networks
C Sakr, N Wang, CY Chen, J Choi, A Agrawal, N Shanbhag, ...
arXiv preprint arXiv:1901.06588, 2019
Il sistema al momento non può eseguire l'operazione. Riprova più tardi.
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