More is Different: Physical Computing Discovery

The heterogenous integration of novel materials with existing technology is critical to beyond Moore computing paradigms. Digital computation based upon silicon has relied heavily on a reductionist hierarchy, wherein the system can be reduced to simple rules and elements can designed separately. However, this hierarchy collapses when novel materials are integrated into physics-based compute paradigms to due to the twin difficulties of complexity and scale. Physical compute systems must be co-designed. In 1972, the physicist Philip W. Anderson coined the term, “more is different” to describe emergent phenomena that occur in complex systems where entirely new properties appear, especially where symmetries are broken¹. Here I will discuss experimental work on building complex physical systems that execute computational kernels, for example outer product updates², and how these systems are “different” from their digital counterparts. Next, I will demonstrate how models can be used to predict the evolution of simple neuromorphic learning systems when operated under certain constraints³. These models break down when phase transformation (or symmetry breaking) occurs and the collective behavior of an ensemble of interacting elements is expected to exhibit emergent phenomena. Finally, I will discuss the development of a computing discovery platform to study emergent materials behavior, wherein rapid synthesis, characterization, and integration of new materials into compute arrays enables studying compute outcomes at scale (&gt;1,000 interacting elements).<br/><b>References</b><br/>[1] Anderson <i>Science</i>, <i>177</i>(4047), 393-396 (1972)<br/>[2] Fuller et. al. <i>Science</i>, 364(6440), 570-574 (2019)<br/>[3] Li et. al. <i>Frontiers in Neuroscience</i>, <i>15</i>, 636127 (2021)