Presentation
3rd ROAD4NN Workshop: Research Open Automatic Design for Neural Networks
TimeSunday, July 10th8am - 5pm PDT
Location3000, Level 3
Event Type
Workshop
AI
EDA
DescriptionIn the past decade, machine learning, especially neural network based deep learning, has achieved an amazing success. Various neural networks, such as CNNs, RNNs, LSTMs, Transformers, BERT, GNNs, 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, Intel Nervana/Habana/Loihi ASICs, Xilinx FPGA, Google TPU, Microsoft Brainwave, Amazon Inferentia, to name just a few. However, there is a significant gap between the fast algorithm evolvement and staggering hardware development, hence calling for broader participation in software-hardware co-design from both academia and industry.
In this workshop, we focus on the research open automatic design for neural networks, a holistic open source approach to general-purpose computer systems broadly inspired by neural networks. More specifically, 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, as well as programming, 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.
In this workshop, we focus on the research open automatic design for neural networks, a holistic open source approach to general-purpose computer systems broadly inspired by neural networks. More specifically, 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, as well as programming, 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.