"One of the things that’s happened in the last 10 to 15 years is that power-scaling has stopped,” he says. Moore’s law — the maxim that processing power will double every 18 months or so — continues, but battery lives just haven’t kept up. “The efficiency of computation is not increasing very rapidly,” he says.
Hammerstom, who helped build chips for Intel back in the 1980s, wants the UPSIDE chips to do computing in a whole different way. He’s looking for an alternative to straight-up boolean logic, where the voltage in a chip’s transistor represents a zero or a one. Hammerstrom wants chipmakers to build analog processors that can do probabilistic math without forcing transistors into an absolute one-or-zero state, a technique that burns energy.
It seems like a new idea — probabilistic computing chips are still years away from commercial use — but it’s not entirely. Analog computers were used in the 1950s, but they were overshadowed by the transistor and the amazing computing capabilities that digital processors pumped out over the past half-century.
Digital Processors Limited by Power; What’s the UPSIDE?
The Unconventional Processing of Signals for Intelligent Data Exploitation (UPSIDE) program seeks to break the status quo of digital processing with methods of video and imagery analysis based on the physics of nanoscale devices. UPSIDE processing will be non-digital and fundamentally different from current digital processors and the power and speed limitations associated with them.
Instead of traditional complementary metal–oxide–semiconductor (CMOS)-based electronics, UPSIDE envisions arrays of physics-based devices (nanoscale oscillators may be one example) performing the “processing. These arrays would self-organize and adapt to inputs, meaning that they will not need to be programmed as digital processors are. Unlike traditional digital processors that operate by executing specific instructions to compute, it is envisioned that the UPSIDE arrays will rely on a higher level computational element based on probabilistic inference embedded within a digital system.
Probabilistic inference is the fundamental computational model for the UPSIDE program. An inference process uses energy minimization to determine a probability distribution to find the object that is the most likely interpretation of the sensor data. It can be implemented directly in approximate precision by traditional semiconductors as well as by new kinds of emerging devices.
