Prize 2016

Inaugural Misha Mahowald Prize for Neuromorphic Engineering won by IBM TrueNorth Project

The Misha Mahowald Prize recognizes outstanding achievement in the field of neuromorphic engineering. Neuromorphic engineering is defined as the construction of artificial computing systems which implement key computational principles found in natural nervous systems. Understanding how to build such systems may enable a new generation of intelligent devices, able to interact in real-time in uncertain real-world conditions under severe power constraints, as biological brains do.

Misha Mahowald, for whom the prize is named, was a charismatic, talented and influential pioneer of neuromorphic engineering whose creative life unfortunately ended prematurely. Nevertheless, her novel designs of brain-inspired CMOS VLSI circuits for vision and computation have continued to influence a generation of engineers.

For the inaugural 2016 prize, the independent jury led by Prof. Terrence Sejnowski of the Salk Institute evaluated 21 entries worldwide. They have selected the TrueNorth project, led by Dr. Dharmendra S. Modha at IBM Research – Almaden in San Jose, California as the winner for 2016:

“For the development of TrueNorth, a neuromorphic CMOS chip that simulates 1 million spiking neurons with connectivity and dynamics that can be flexibly programmed while consuming only 70 milliwatts. This scalable architecture sets a new standard and brings us closer to achieving the high levels of performance in brains.”

The TrueNorth architecture is a milestone in the development of neuromorphic processors because it achieves the combination of scale, ultra-low-power and high performance that has never before been demonstrated in a real neuromorphic system. It is the first neuromorphic system that can compete with conventional state-of-the-art von Neumann processors on real-world problems on an equal footing. In doing this, it opens the door to future orders-of-magnitude improvements in computing power that will no longer be possible using the von Neumann architecture as its inherent bottlenecks approach physical limits.