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ORCID1st EMC2@ASPLOS 2018: Williamsburg, VA, USA
- 1st Workshop on Energy Efficient Machine Learning and Cognitive Computing for Embedded Applications, EMC2@ASPLOS 2018, Williamsburg, VA, USA, March 25, 2018. IEEE 2018, ISBN 978-1-5386-7367-6
- Kamal Khouri:
Keynote Abstract: Safety and Security at the Heart of Autonomous Driving. 1 - Charles Qi:
Invited Talk Abstract: Challenges and Solutions for Embedding Vision AI. 2 - Grigori Fursin:
Invited Talk Abstract: Introducing ReQuEST: An Open Platform for Reproducible and Quality-Efficient Systems-ML Tournaments. 3 - Karl Taht, Surya Narayanan, Rajeev Balasubramonian:
A Case for Dynamic Activation Quantization in CNNs. 4-8 - Aliasger Zaidy, Andre Xian Ming Chang, Vinayak Gokhale, Eugenio Culurciello:
A High Efficiency Accelerator for Deep Neural Networks. 9-13 - Tao Sheng, Chen Feng, Shaojie Zhuo, Xiaopeng Zhang, Liang Shen, Mickey Aleksic:
A Quantization-Friendly Separable Convolution for MobileNets. 14-18 - Seyed Hamed Fatemi Langroudi, Tej Pandit, Dhireesha Kudithipudi:
Deep Learning Inference on Embedded Devices: Fixed-Point vs Posit. 19-23 - Andre Xian Ming Chang, Aliasger Zaidy, Eugenio Culurciello:
Efficient Compiler Code Generation for Deep Learning Snowflake Co-Processor. 24-28 - C. D. Tharindu Mathew
, Aswin Raghavan, Sek Chai:
Event Prediction in Processors Using Deep Temporal Models. 29-33 - Sumanth Gudaparthi, Surya Narayanan, Rajeev Balasubramonian:
Moving CNN Accelerator Computations Closer to Data. 34-38
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