As the demand for big data increases and the speed of traditional CPUs cannot keep pace, new computing paradigms and architectures are needed to meet the demands for our data hungry world. To keep pace with this, Ising Computing and probabilistic computing have emerged as a method to solve NP-Hard optimization problems (such as logistics, place and route in circuits), perform Machine Learning training and inference, model decision making in animal brains, and much more.

In this talk, we demonstrate the Parallel Asynchronous Stochastic Sampler (PASS), a neuromorphic, clock-free accelerator that mimics brain-like asynchronous computation using the Ising Model. This has the potential for orders of magnitude speed increase over traditional methods for solving these problems while being the first on-chip, fully CMOS, demonstration of such an architecture. We show how the usage of the PASS accelerator in a variety of spaces including modeling of quantum systems, multiplier-free machine learning, combinatorial optimization, and simulation of decision making in animal brains. This new class of probabilistic accelerator has wide reaching applications in a variety of fields, ushering in a new type of computing paradigm.

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