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ORCID2nd EMC2@HPCA 2019: Washington, DC, USA
- 2nd Workshop on Energy Efficient Machine Learning and Cognitive Computing for Embedded Applications, EMC2@HPCA 2019, Washington, DC, USA, February 17, 2019. IEEE 2019, ISBN 978-1-7281-6763-3
- Partha Maji, Andrew Mundy, Ganesh Dasika, Jesse G. Beu, Matthew Mattina, Robert D. Mullins
:
Efficient Winograd or Cook-Toom Convolution Kernel Implementation on Widely Used Mobile CPUs. 1-5 - Sek M. Chai, Kilho Son, Jesse Hostetler:
Bootstrapping Deep Neural Networks from Approximate Image Processing Pipelines. 6-10 - Xinfeng Xie, Xing Hu, Peng Gu, Shuangchen Li, Yu Ji, Yuan Xie:
NNBench-X: A Benchmarking Methodology for Neural Network Accelerator Designs. 11-15 - Cheng-En Wu, Yi-Ming Chan, Chu-Song Chen:
On Merging MobileNets for Efficient Multitask Inference. 16-20 - Farzad Farshchi, Qijing Huang, Heechul Yun:
Integrating NVIDIA Deep Learning Accelerator (NVDLA) with RISC-V SoC on FireSim. 21-25 - Urmish Thakker, Jesse G. Beu, Dibakar Gope, Ganesh Dasika, Matthew Mattina:
Run-Time Efficient RNN Compression for Inference on Edge Devices. 26-30 - Sree Harsha Nelaturu, Ziheng Wang, Saman P. Amarasinghe:
Accelerated CNN Training through Gradient Approximation. 31-35 - Dawit Aboye, Dylan Kupsh, Maggie Lim, Jacqueline Mai, Deeksha Dangwal, Diba Mirza, Timothy Sherwood:
PyRTLMatrix: An Object-Oriented Hardware Design Pattern for Prototyping ML Accelerators. 36-40
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