Research in neuromorphic sensing and computing

In spite of the remarkable technological advances in micro and nano-scale integration, the performance achieved by specialized biological sensing systems makes even the most advanced man-made systems of today look crude and primitive. At the fundamental level most of the sensory processing in biology is inherently ``analog” and efficiency arises out of exploitation of computing and sensing primitives inherent in the device physics, like diffusion or feedback regulation. Also, unlike man-made sensors which consider device and sensor noise as nuisances, biology has evolved to use non-linear sensing techniques to exploit noise to its advantage and operate at or below fundamental limits. We are researching novel neuromorphic architectures and processors, novel spiking neuron models, noise-exploitation techniques, and hybrid bio-silicon cyborg systems. 

Research in self-powered sensing and battery-less circuits and systems

Self-powered sensing refers to an energy scavenging paradigm where the operational power of a sensor is harvested directly from the signal being sensed. For example, a piezoelectric transducer could be used for sensing variations in mechanical strain and the energy in the strain variations could also be used for the computation and storage. As a result, the operation of the self-powered sensor can be asynchronous where events of interest can directly energize the computing and storage circuits. In this manner, the asynchronous sensor can continuously monitor for events of interest without experiencing any down-time, a feature that can’t be guaranteed with conventional synchronous energy scavenging approaches. Our approach to self-powered sensing is to investigate analog non-volatile storage techniques that operate at fundamental limits of energy scavenging and hence can directly be energized by a transducer like a piezoelectric element. Based on this principle we have reported different variants of self-powered chipsets that can be used for health and usage monitoring of mechanically active parts like biomechanical implants and structures. We are also investigating perennial computing devices that can operate by harvesting energy from thermal noise and local field potential in the brain. Because the power levels of thermal noise are typically less than 1 femtowatt, conventional electronic cannot even operate, let alone scavenge energy. We have researching self-powered timers and neural activity recording devices that operate only using ambient thermal-noise.

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