Thrust 2: Circuits

To enable the design of circuit topology and circuit parameters, we use AI-enhanced evolutionary optimization and reinforcement learning. AI/ML techniques can be used to optimize multiple objectives; in this case, we may be interested in how accurate the circuit performs the task using a given device model, while simultaneously minimizing latency and/or energy usage. We leverage abstracted device models for faster iteration over the design space. AI/ML methods can also be used to evolve both the topology of the circuit and the parameters simultaneously. Evolutionary algorithms can potentially be used to creatively discover new ways to design circuits or leverage the underlying device characteristics and have been found to have surprising creativity in the design of novel solutions to a variety of problems. On the other hand, reinforcement learning techniques can leverage existing designs and domain knowledge for circuit design. Further Bayesian and evolutionary multi-objective optimization can further help optimize our fitness function/reward functions for the evolutionary and reinforcement learning algorithms respectively.

We anticipate the AI/ML-guided design will also inform design of our stochastic devices and enable the design of creative circuits and architectures for our application. We will develop key RNG scientific kernels that exploit probabilistic devices and algorithms. These simulators and scientific kernels will be critical building blocks to feed into the architecture design search.