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
Conference paper
Publications
56 results for Kailash Gopalakrishnan
Accumulation bit-width scaling for ultra-low precision training of deep networks
- Charbel Sakr
- Naigang Wang
- et al.
- 2019
- ICLR 2019
Conference paperTraining deep neural networks with 8-bit floating point numbers
- Naigang Wang
- Jungwook Choi
- et al.
- 2018
- NeurIPS 2018
Conference paperA Scalable Multi-TeraOPS Core for AI Training and Inference
- Sunil Shukla
- Bruce Fleischer
- et al.
- 2018
- IEEE SSC-L
PaperA Scalable Multi-TeraOPS Deep Learning Processor Core for AI Trainina and Inference
- Bruce Fleischer
- Sunil Shukla
- et al.
- 2018
- VLSI Circuits 2018
Conference paperTrue Gradient-Based Training of Deep Binary Activated Neural Networks Via Continuous Binarization
- Charbel Sakr
- Jungwook Choi
- et al.
- 2018
- ICASSP 2018
Conference paperExploiting approximate computing for deep learning acceleration
- Chia-Yu Chen
- Jungwook Choi
- et al.
- 2018
- DATE 2018
Conference paperADaComP: Adaptive residual gradient compression for data-parallel distributed training
- Chia-Yu Chen
- Jungwook Choi
- et al.
- 2018
- AAAI 2018
Conference paperPOSTER: Design Space Exploration for Performance Optimization of Deep Neural Networks on Shared Memory Accelerators
- Swagath Venkataramani
- Jungwook Choi
- et al.
- 2017
- PACT 2017
Conference paperINVITED: Accelerator Design for Deep Learning Training: Extended Abstract: Invited
- Ankur Agrawal
- Chia-Yu Chen
- et al.
- 2017
- DAC 2017
Conference paper