With the rise of DL, our world braces for AI in every edge device, creating an urgent need for edge-AI SoCs. This SoC hardware needs to support high throughput, reliable and secure AI processing at ULP, with a very short time to market. With its strong legacy in edge solutions and open processing platforms, the EU is well positioned to become leader in this SoC market. However, this requires AI edge processing to become at least 100 times more energy-efficient, while offering sufficient flexibility and scalability to deal with AI as a fast-moving target. Since the design space of these complex SoCs is huge, advanced tooling is needed to make their design tractable. The CONVOLVE project addresses these roadblocks. It takes a holistic approach with innovations at all levels of design hierarchy. Starting with an overview of SOTA DL processing support and our project methodology, this paper presents 8 important design choices largely impacting energy-efficiency and flexibility of DL hardware. Finding good solutions is key in making smart-edge computing reality.
翻译:随着DL的崛起,我们的世界在每一个边缘设备中都支持AI, 从而产生了对边缘AI SoCs的迫切需要。 这个 SoC硬件需要支持ULP的高输送量、可靠和安全的AI处理, 并且需要很短的市场时间。 由于在边缘解决方案和开放的处理平台中留下大量遗产, 欧盟完全有能力成为这个SOC市场的领先者。 但是, 这要求AI边缘处理至少要提高100倍的能源效率, 并提供足够的灵活性和可扩展性, 将AI作为快速移动的目标。 由于这些复杂的 SoCs的设计空间巨大, 需要先进的工具才能使其设计具有可移动性。 CONOVOLVE项目解决了这些路障问题。 它采取整体性的方法, 在所有层次的设计结构层次上都有创新。 从SOTA DL处理支持和我们的项目方法的概览开始, 本文提出了8项重要的设计选择, 在很大程度上影响到DL硬件的能源效率和灵活性。 找到良好的解决方案是使智能计算成为现实的关键。