A computer chip modeled after the human brain just solved physics equations that normally require room-sized supercomputers burning through megawatts of power — and it did it using a fraction of the energy. The study, published in Nature Machine Intelligence, comes from researchers Brad Theilman and Brad Aimone at Sandia National Laboratories.
Neuromorphic computing — literally “shaped like the brain” — uses chips designed to mimic biological neurons. Unlike traditional computers that shuttle data between separate processor and memory units, neuromorphic chips integrate everything together. They communicate through spikes (bursts of electrical signals like real neurons firing) and only consume power when a spike happens. Your brain runs on about 20 watts, less than a light bulb. A modern supercomputer can consume 20 megawatts — a million-to-one ratio.
The breakthrough is that Sandia proved neuromorphic hardware can solve partial differential equations (PDEs) — the mathematical workhorses behind weather forecasting, bridge engineering, nuclear reactor modeling, and virtually all scientific simulation. Until now, most researchers assumed brain-like chips were limited to pattern recognition tasks and couldn’t handle real math.
The key insight was that a well-known computational neuroscience model of cortical brain circuits has a “non-obvious” mathematical connection to PDEs. This model had existed for 12 years before anyone realized its deep link to physics equations. By mapping this brain model onto neuromorphic hardware, the team created a PDE solver that works fundamentally differently from traditional approaches.
The landscape of neuromorphic hardware is growing rapidly. IBM’s TrueNorth (2014) packed a million neurons on a single chip running on just 70 milliwatts. Intel’s Hala Point system (2024) crams 1.15 billion neurons into a microwave-oven-sized chassis consuming only 2,600 watts. Europe’s BrainScaleS uses analog circuits that operate a thousand times faster than biological neurons.
The implications extend beyond energy savings. If neuromorphic computers can run physics simulations hundreds of times more efficiently, it transforms fields like climate modeling, drug discovery, and national security. And perhaps most profoundly, by building computers that look like brains, we might finally learn what brains are actually computing — potentially shedding light on neurological diseases like Alzheimer’s and Parkinson’s.