The potential of probabilistic computers
The rise of artificial intelligence (AI) and machine learning (ML) has created a crisis in computing and a significant need for more hardware that is both energy-efficient and scalable. A key step in both AI and ML is making decisions based on incomplete data, the best approach for which is to output a probability for each possible answer. Current classical computers are not able to do that in an energy-efficient way, a limitation that has led to a search for novel approaches to computing. Quantum computers, which operate on qubits, may help meet these challenges, but they are extremely sensitive to their surroundings, must be kept at extremely low temperatures and are still in the early stages of development.
Kerem Camsari, an assistant professor of electrical and computer engineering (ECE) at UC Santa Barbara, believes that probabilistic computers (p-computers) are the solution. P-computers are powered by probabilistic bits (p-bits), which interact with other p-bits in the same system. Unlike the bits in classical computers, which are in a 0 or a 1 state, or qubits, which can be in more than one state at a time, p-bits fluctuate between positions and operate at room temperature. In an article published in Nature Electronics, Camsari and his collaborators discuss their project that demonstrated the promise of p-computers.
“We showed that inherently probabilistic computers, built out of p-bits, can outperform state-of-the-art software that has been in development for decades,” said Camsari, who received a Young Investigator Award from the Office of Naval Research earlier this year.
Camsari’s group collaborated with scientists at the University of Messina in Italy, with Luke Theogarajan, vice chair of UCSB’s ECE Department, and with physics professor John Martinis, who led the team that built the world’s first quantum computer to achieve quantum supremacy. Together the researchers achieved their promising results by using classical hardware to create domain-specific architectures. They developed a unique sparse Ising machine (sIm), a novel computing device used to solve optimization problems and minimize energy consumption.
Camsari describes the sIm as a collection of probabilistic bits which can be thought of as people. And each person has only a small set of trusted friends, which are the “sparse” connections in the machine.
“The people can make decisions quickly because they each have a small set of trusted friends and they do not have to hear from everyone in an entire network,” he explained. “The process by which these agents reach consensus is similar to that used to solve a hard optimization problem that satisfies many different constraints. Sparse Ising machines allow us to formulate and solve a wide variety of such optimization problems using the same hardware.”
The team’s prototyped architecture included a field-programmable gate array (FPGA), a powerful piece of hardware that provides much more flexibility than application-specific integrated circuits. More