BiScaled-DNN: Quantizing long-tailed datastructures with two scale factors for deep neural networks
- Shubham Jain
- Swagath Venkataramani
- et al.
- 2019
- DAC 2019
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29 results for Jungwook Choi
DLFloat: A 16-b Floating Point Format Designed for Deep Learning Training and Inference
- Ankur Agrawal
- Bruce Fleischer
- et al.
- 2019
- ARITH 2019
A Compiler for Deep Neural Network Accelerators to Generate Optimized Code for a Wide Range of Data Parameters from a Hand-crafted Computation Kernel
- Eri Ogawa
- Kazuaki Ishizaki
- et al.
- 2019
- COOL CHIPS 2019
Accumulation bit-width scaling for ultra-low precision training of deep networks
- Charbel Sakr
- Naigang Wang
- et al.
- 2019
- ICLR 2019
Training deep neural networks with 8-bit floating point numbers
- Naigang Wang
- Jungwook Choi
- et al.
- 2018
- NeurIPS 2018
A Scalable Multi-TeraOPS Core for AI Training and Inference
- Sunil Shukla
- Bruce Fleischer
- et al.
- 2018
- IEEE SSC-L
A Scalable Multi-TeraOPS Deep Learning Processor Core for AI Trainina and Inference
- Bruce Fleischer
- Sunil Shukla
- et al.
- 2018
- VLSI Circuits 2018
True Gradient-Based Training of Deep Binary Activated Neural Networks Via Continuous Binarization
- Charbel Sakr
- Jungwook Choi
- et al.
- 2018
- ICASSP 2018
PROMISE: An end-to-End design of a programmable mixed-Signal accelerator for Machine-Learning algorithms
- Prakalp Srivastava
- Mingu Kang
- et al.
- 2018
- ISCA 2018
Compensated-DNN: Energy efficient low-precision deep neural networks by compensating quantization errors
- Shubham Jain
- Swagath Venkataramani
- et al.
- 2018
- DAC 2018