Energy-Efficient Programmable Accelerators

50年来，计算系统一直受益于每美元性能的指数增长。第一个微处理器于1971年使用10微米晶体管制造，而最近的设计则使用14纳米晶体管制造。由此产生的晶体管密度增加了五个数量级，加上时钟频率增加了三个数量级，使计算能够从仅处理简单的业务计算转变为支持机器学习算法，在准确性上与人类相媲美，并导致新兴的“物联网”，有望广泛提高便利性和生产力。 * 然而，在5nm工艺节点之后，晶体管的缩放速度可能会显着放缓，威胁到未来的愿景。事实上，晶体管阈值电压缩放基本上在十年前就停止了，导致时钟频率停滞，转向多核以及最近对“暗硅”的担忧。虽然替代技术（例如，量子计算）可以提供极好的长期解决方案，这些解决方案将花费数十年的时间来完全开发，从而导致"休耕期"，在该"休耕期"期间，除非找到替代方案，否则计算系统能力将停滞。 这种承诺是以降低灵活性和/或困难的编程模型为代价的。 * 本研究计划的长期目标是，与当今的计算硬件相比，为各种软件应用程序实现单位美元计算能力的数量级改进。 这种改善几乎肯定会使企业和整个社会受益。 例如，这种增长可以使社会受益，使更复杂的机器学习能够应用于嵌入式物联网设备。 计算能力的数量级增益可以转化为物联网冰箱之间的差异，如果牛奶即将在日期之前到达使用位置，则会向您发送电子邮件，并且可以识别您对食物选择感到厌倦，并根据过去的偏好和您家庭的健康目标建议购物清单。该研究计划将通过探索软件和硬件方法来实现这些目标，以改善软件开发的便利性和通过利用专用硬件来提高计算能力之间的权衡。 预期的结果是深入了解如何最好地构建未来的计算系统，使目前强大的软件行业在摩尔定律结束后仍然可行。 加拿大将从中受益，因为越来越多的计算机硬件（英特尔、AMD、高通）和软件（Electronic Arts、IBM、微软）公司在这里设有研发机构，可以随时利用这些见解。

For 50 years computing systems have benefited from exponentially increasing performance per dollar. The first microprocessor was fabricated using 10 micrometer transistors in 1971 while recent designs have been manufactured using 14 nanometer transistors. The resulting five-orders of magnitude increase in transistor density combined with three orders of magnitude increase in clock frequency has enabled computing to change from handling only simple business calculations to supporting machine learning algorithms rivaling humans in accuracy and led to the emerging "internet of things" that promises to broadly increase convenience and productivity. ***However, the rate of transistor scaling will likely slow significantly after the 5nm process node threatening such visions for the future. Indeed, transistor threshold voltage scaling essentially stopped a decade ago resulting in stagnant clock frequencies, a shift to multicore and more recent concerns about "dark silicon". While alternative technologies (e.g., quantum computing) may provide excellent long term solutions these will take decades to fully develop resulting in a "fallow period" during which computing system capability will stagnate unless alternatives are found. In principle, computing system capability can be improved by orders of magnitude by exploiting specialized hardware. This promise comes at the expense of reduced flexibility and/or difficult programming models. ***The long-term goal of this research program is to enable order-of-magnitude improvements in computing capability per dollar versus today's computing hardware for diverse software applications. Such an improvement will almost certainly benefit business and society in general. For example, such increases could benefit society enabling more sophisticated machine learning to be applied embedded internet-of-things devices. An order of magnitude gain in computing capability could translate into the difference between a IoT enabled fridge that emails you if the milk is about to reach its use before date, and one that recognizes you are getting bored with your food choices and suggests a shopping list based upon past preferences and your family's health goals.***This research program will tackle these goals by exploring both software and hardware approaches to improve the tradeoff between ease of software development and increasing computing capability by exploiting specialized hardware. The expected outcome is insights into how best to structure future computing systems so that the currently robust software industry remains viable past the end of Moore's Law. Canada will benefit because there is a growing number of computer hardware (Intel, AMD, Qualcomm) and software (Electronic Arts, IBM, Microsoft) companies with an R&D presence here that can readily leverage these insights.**