Phase I: laconic: a deep learning accelerator for energy efficiency and high-performance for edge devices

机器学习正在改变商业、医学、科学以及现代生活的方方面面。加拿大一直处于机器学习创新的前沿，因为我们几十年来一直在该领域稳步投资。如果没有现代商用硬件能够提供的计算能力，机器学习是不可能实现的。进一步的创新将不得不在很大程度上依赖于计算能力的进步，从而提高效率。**在4年多的时间里，我们一直在开发计算硬件概念和设备设计，为深度学习（DL）提供比普通硬件快一个数量级的数据处理能力和能效。凭借我们在高性能计算系统设计技术方面20多年的经验，我们超越了第一代优化，转而投资于高风险的补充技术。我们的方法是识别和利用深度学习价值流的典型但基本的属性。结果超出了我们的预期。**我们的努力最终形成了Laconic，这是一款比其他加速器性能高出一个数量级的设计。Laconic利用深度学习模型中的基本底层值属性，不需要程序员的帮助。虽然Laconic可以配置为针对多个细分市场，但我们将瞄准“边缘”（嵌入式/移动）应用，因为我们之前资助的NSERC市场评估显示了巨大的潜力。我们的主要竞争优势在于，Laconic将允许：1)部署更复杂、更精确的深度学习网络，2)大幅降低能耗，从而提高移动设备的正常运行时间，同时使系统在各种情况下更加便携和可用。我们已经通过早期设计探索的行业标准方法证明了Laconic的潜力。我们正在寻求第一阶段的支持，以建立一个概念验证演示原型，这对于吸引合作伙伴和投资组织的兴趣至关重要（有些已经表达了强烈的兴趣）。****

Machine Learning is transforming commerce, medicine, science, and virtually every aspect of modern life. Canada has been at the forefront of innovation in Machine Learning as we have been steadily investing in the area for decades. Machine learning would not have been possible without the computing power that modern commodity hardware was able to deliver. Further innovation will have to rely heavily in advances in computational power and thus efficiency.** Over the course of 4+ years we have been developing computing hardware concepts and device designs that deliver an order of magnitude faster data processing capability and energy efficiency than commodity hardware for Deep Learning (DL). With our 20+ years of experience in designing techniques for high-performance computing systems, we leapfrogged over first generation optimizations and instead invested in high-risk complementary techniques. Our approach was to identify and exploit typical yet fundamental properties of the value stream of Deep Learning. The results surpassed our expectations.** Our efforts culminated into Laconic, a design that outperforms other accelerators by an order of magnitude. Laconic exploits fundamental low-level value properties in Deep Learning models and requires no help from the programmer. While Laconic can be configured to target multiple market segments, we will target "edge" (embedded/mobile) applications as our previously-funded NSERC Market Assessment has shown great potential there. Our key competitive advantage is that Laconic will allow: 1) the deployment of more sophisticated and accurate DL networks, 2) a drastic reduction in energy consumption and thus improvement in up-time for mobile devices while making the system more portable and usable in a variety of scenarios. We have proven the potential of Laconic through industry standard methodologies for early design exploration. We are seeking Phase I support to build a proof-of-concept demonstration prototype which is essential for attracting interest from partner and investment organizations (some already express strong interest).****