6 papers
Understanding and Embracing Imperfection in Physical Learning Networks
Sam Dillavou, Marcelo Guzman, Andrea J. Liu +1
Performing machine learning with analog signals offers advantages in speed and energy efficiency, but sensitivity to component and measurement imperfections often foils training wi…
Structural aging of a cohesive and amorphous granular solid under cyclic loading
William Hobson-Rhoades, Douglas J Durian, Yue Fan +1
We investigate how cyclic loading evolves the structure and deformation behaviors of a granular raft composed of particles floating at an air-oil interface. The raft has a disorder…
Collective Behavior and Memory States in Flow Networks with Tunable Bistability
Lauren E. Altman, Nadia Aguilar, Douglas J. Durian +2
Multistability-induced hysteresis has been widely studied in mechanical systems, but such behavior has proven more difficult to reproduce experimentally in flow networks. Natural f…
Analog Physical Systems Can Exhibit Double Descent
Sam Dillavou, Jason W Rocks, Jacob F Wycoff +2
An important component of the success of large AI models is double descent, in which networks avoid overfitting as they grow relative to the amount of training data, instead improv…
Solving the compute crisis with physics-based ASICs
Maxwell Aifer, Zach Belateche, Suraj Bramhavar +11
Escalating artificial intelligence (AI) demands expose a critical "compute crisis" characterized by unsustainable energy consumption, prohibitive training costs, and the approachin…
Stochastic dynamics of granular hopper flows: a configurational mode controls the stability of clogs
David Hathcock, Sam Dillavou, Jesse M. Hanlan +2
Granular flows in small-outlet hoppers exhibit several characteristic but poorly understood behaviors: temporary clogs (pauses) where flow stops before later spontaneously restarti…