Section: New Results

3D-Stacked Implementation of Neural Networks

In order to cope with increasingly stringent power and variability constraints, architects need to investigate alternative paradigms. Neuromorphic architectures are increasingly considered (especially spike-based neurons) because of their inherent robustness and their energy efficiency. Yet, they have two limitations: the massive parallelism among neurons is hampered by 2D planar circuits, and the most cost-effective hardware neurons are analog implementations that require large capacitors, We show that 3D stacking with Through-Silicon-Vias applied to neuromorphic architectures can solve both issues: not only by providing massive parallelism between layers, but also by turning the parasitic capacitances of TSVs into useful capacitive storage.