Suhas Kumar obtained a PhD from Stanford University in Electrical Engineering in 2013. He now heads the R&D department at Rain AI, a semiconductor research startup in the San Francisco Bay area. His research focuses on building brain-inspired electronic devices and designing novel algorithms to use such devices. His research is aimed at reducing AI’s energy consumption by addressing underlying fundamental

Abstract: Electronics Devices at the Edge of Chaos

Abstract: Artificial Intelligence (AI) has an energy problem, which is both economically and environmentally unsustainable. The fundamental cause of this problem is the primitive thermodynamic computing principles used in digital processors. The brain offers a fantastic alternative by employing complexity within every neuron, wherein information is processed parallelly by multiple kinetic degrees of freedom, thereby making the system highly efficient. In this talk, I will discuss ways to engineer complexity into the kinetics and material physics within electronic components. I will talk about how an extreme form of complexity, known as Edge of Chaos, enables interesting behaviors within a single component (e.g., neuron-like bursting), which would typically take hundreds of traditional digital components to simulate.