Hardware/Software Co-Design for Machine Learning Accelerators

从自动语言翻译到人脸检测，人工智能(AI)主导着我们的日常生活。这一成功归功于人工神经网络的广泛采用。这些网络受到大脑的启发，通过让机器自动学习来解决任务，而不需要事先编程。这种机器学习方法已经有30多年的历史了；然而，到目前为止，还没有足够的计算能力来处理实际大小的问题。 高性能并行处理器的广泛采用，如图形处理单元(GPU)，使得能够解决实际大小问题的复杂神经网络的发展成为可能。这引发了深度学习革命，这场革命将彻底改变从机器人到商业的几乎所有领域。然而，这些新网络需要能够提供更强大的计算能力的设备，更重要的是，具有高能效。这些工作负载大多运行在云上或互联网边缘(例如移动设备)上，而这些设备的能耗是首要考虑因素。因此，公司之间开始了一场新的竞赛，开始设计专门用于机器学习的最节能的加速器。 新硬件的开发是一个非常耗时、多年的过程。这涉及到大量的手动设计和测试，需要大量的设计团队。此外，硬件公司倾向于采取保守的方法，为众所周知的、公认的机器学习技术设计新的硬件。这阻碍了新机器学习技术的采用，因为硬件总是为过时的技术而设计的。 这项提议的长期目标是制定一套完全自动化加速器设计的共同原则，短期五年目标特别侧重于神经网络。这既包括自动化硬件设计，也包括软件接口设计，在社区中称为联合设计。当新的加速器出现时，只需将其作为旧加速器的替代品投入使用，即可开箱即用。这也将导致从设计到硬件的快速周转时间，这最终意味着加速器更加多样化，每个加速器都专门针对特定类型的问题。这将使新的革命性机器学习应用程序得以采用，否则这些应用程序可能永远不会出现。 加拿大被许多人认为是机器学习的领导者，其研究人员在该领域取得了许多进展。鉴于专门硬件在支持机器学习应用方面的重要性，该研究计划将通过培训下一代专注于系统的机器学习专家来加强这一地位。我们打算将我们的所有软件作为开源软件发布，以鼓励研究人员和行业参与者采用我们的技术。

Artificial intelligence (AI) dominates our daily life, from automatic language translation to face detection. This success is due to the widespread adoption of artificial neural networks. These networks are inspired by the brain and solve tasks by letting the machine learn automatically, without requiring to be programmed beforehand. This machine learning approach has been known for over 30 years; however, up until now, there were simply not enough computational power available to handle real size problems. The widespread adoption of high performance parallel processors, such as Graphics Processing Units (GPUs), has enabled the development of complex neural networks able to solve real size problems. This has triggered the deep learning revolution, which is set to revolutionize virtually every field, from robotics to business. However, these new networks require devices capable of delivering even greater computational power, and more importantly, with high energy efficiency. Most of these workloads runs on the cloud or on Internet's edge (e.g. mobile devices) where energy consumption is the prime concern. As a result, a new race among companies has started to design the most energy efficient accelerators specialized for machine learning. The development of new hardware is a very time consuming, multi-years, process. This involves a lot of manual design and testing requiring large teams of designers. Furthermore, hardware companies tend to take a conservative approach and design their new hardware for well known, well established machine learning techniques. This hinders the adoption of new machine learning techniques since hardware is always designed for obsolete techniques. The long term goal of this proposal is to develop a set of common principles for fully automating the design of accelerators, with the short term five years goal focusing on neural networks in particular. This involves both automating hardware design as well as software interface design, known in the community as co-design. When a new accelerator emerges, it can simply be dropped in as a replacement for the older one and will just work out of the box. This will also lead to a rapid turnaround time from design to hardware, which ultimately means more accelerator diversity, each specialized for a particular type of problems. This will enable the adoption of new revolutionary machine learning applications that might otherwise never see daylight. Canada is considered by many as the leader for machine learning and its researchers have pioneered many advances in the field. Given the importance that specialized hardware plays in enabling machine learning applications, this research program will strengthen this position on the system side by training the next generation of system-focused machine learning expert. We intend to release all our software as open source, to encourage uptake of our techniques among researchers and industry players.