Compact Interconnect Approach For Networks Of Neural Cliques Using 3d Technology

Thanks to their brain-like properties, neural networks outperform traditional algorithms in certain group of applications. However, since they are wire-dominated systems, their hardware implementation poses numerous challenges as high latency and energy consumption. The recent technological improvements allow for stacking few dies one on another and designing 3D electronic circuits. This creates opportunities for 3D efficient implementations of neural networks targeting high-performance applications. This work explores the gains of 3D technology for neural networks relying on neural cliques. A general study shows up to 55% reduction in terms of total interconnect length and interconnect power consumption, and 74% reduction of the maximal interconnect delay. The proposed approach is validated with a power management applicative test-case. We demonstrate that, in this scenario, the 3D architecture reduces interconnect length and power by 35% and the maximal delay by 57%, compared to 2D.