SUNDAY July 19, 8:00am - 4:00pm
Workshop 4: ROAD4NN: Research Open Automatic Design for Neural Networks
Zhenman Fang - Simon Fraser University, Burnaby, Canada
Yanzhi Wang - Northeastern Univ. , Boston, MA
Zhe Chen - Univ. of California, Los Angeles, CA
In the past decade, machine learning, especially neural network based deep learning, has achieved an amazing success. Various neural networks (NNs), such as CNNs, RNNs, LSTMs and SNNs, have been deployed for various industrial applications like image classification, speech recognition, and automated control. On one hand, there is a very fast algorithm evolvement of neural network models, almost every week there is a new model from a major academic and/or industry institute. On the other hand, all major industry giants have been developing and/or deploying specialized hardware platforms to accelerate the performance and energy-efficiency of neural networks across the cloud and edge devices. This include Nvidia GPU, ARM Embedded CPUs, Qualcomm Adreno Embedded GPUs, Intel Nervana/Habana/Loihi ASICs, Intel and Xilinx FPGA, Google TPU, Microsoft Brainwave, Amazon Inferentia, Huawei Da Vinci architecture, and Cambricon NPU, to name just a few. However, there is a significant gap between the fast algorithm evolvement and staggering hardware development.
In this workshop, we focus on the open research problems of automatic design for neural networks, where we discuss full stack open source infrastructure support to develop and deploy novel neural networks, including novel algorithms and applications, hardware architectures and emerging devices, hardware-software codesign, as well as programming, compiler, system, and tool support. We plan to bring together academic and industry experts to share their experience, discuss challenges they face as well as potential focus areas for the community. Workshop topics include, but are not limited to:◾ New algorithm advancement of neural networks, ◾ Bio-plausible neural network models, ◾ Neural network model compression and quantization, ◾ Application of neural networks into new areas, ◾ Hardware acceleration and architecture for neural networks, ◾ New circuits and devices for neural networks, ◾ Abstraction to bridge the algorithm and hardware gap for neural networks, ◾ Compilation, programming language, and design automation support to map neural networks to hardware platforms, ◾ System support to deploy neural networks in cloud and edge devices, ◾ Benchmarks for various neural network models and hardware accelerators, ◾ Performance comparison of neural networks running in difference architectures and devices, ◾ Other research infrastructures that enable the above studies.
There will be a keynote given by Prof. Deming Chen, UIUC, who also co-founded Inspirit IoT, Inc. We have also invited 11 renowned researchers from academic and/or industry to give invited talks. For free registration and more information on the workshop schedule, please visit: