Mikko Lipasti is the Philip Dunham Reed Professor of Electrical and Computer Engineering at the University of Wisconsin-Madison.
Prof. Lipasti is an established expert in the design of high-performance, low-power, and reliable processor cores; networks-on-chip for many-core processors; and fundamentally new, biologically-inspired models of computation. He was named an IEEE Fellow (class of 2013) "for contributions to the microarchitecture and design of high-performance microprocessors and computer systems." He has published over 100 refereed papers, advised 17 Ph.D. theses to completion, and is a charter member of the ISCA, MICRO, and HPCA Halls of Fame. Over the last ten years, he focused much of his research program on developing a deep understanding of neural and cortical algorithms, specifically unsupervised and semi-supervised approaches for vision (object recognition), as well as efficient synthetic algorithms and implementations that mimic their biological counterparts.
In 2012, he co-founded Thalchemy Corp, a venture-funded startup company that is developing novel algorithms and accelerators to enable ultra low-power continuous sensory processing in energy-limited, internet-connected platforms. As the domain of cyber-physical computing moves from disconnected, sparse, and off-line acquisition of sensory data to massive complexes of interconnected devices that generate reams of data in real time, the demand for efficient and intelligent algorithms and processing substrates for such systems will grow exponentially. In response to this demand, Lipasti's research group, along with Thalchemy, is developing technology that mimics the operation of the mammalian thalamus, which serves as the routing, filtering, and preprocessing point for all sensory data on its way to the neocortex. The mammalian nervous system is unmatched in its capability for extracting salient information from a flood of unstructured sensory data, while consuming minimal amounts of energy. Adopting the intelligent, event-driven, and extremely power-efficient algorithms observed in nature, and deploying them in hardware and software in the first tier of future sensing systems will enable deployment of intelligent sensing and processing capabilities for massive real-time data streams in energy- and power-constrained environments, with applications ranging from healthcare, environmental sensing, safety and structural monitoring, all the way to entertainment and human-friendly user interfaces. Lipasti's decade of experience in developing synthetic systems inspired by their biological sensing counterparts will play a critical role in this transition to systems that efficiently sense and process these massive data streams.
He leads the PHARM research team, which is part of the UW-Madison Computer Architecture Group at the University of Wisconsin-Madison.