3D-DPE: A 3D high-bandwidth dot-product engine for high-performance neuromorphic computing

We present and experimentally validate 3D-DPE, a general-purpose dot-product engine, which is ideal for accelerating artificial neural networks (ANNs). 3D-DPE is based on a monolithically integrated 3D CMOS-memristor hybrid circuit and performs a high-dimensional dot-product operation (a recurrent and computationally expensive operation in ANNs) within a single step, using analog current-based computing. 3D-DPE is made up of two subsystems, namely a CMOS subsystem serving as the memory controller and an analog memory subsystem consisting of multiple layers of high-density memristive crossbar arrays fabricated on top of the CMOS subsystem. Their integration is based on a high-density area-distributed interface, resulting in much higher connectivity between the two subsystems, compared to the traditional interface of a 2D system or a 3D system integrated using through silicon vias. As a result, 3D-DPE's single-step dot-product operation is not limited by the memory bandwidth, and the input dimension of the operations scales well with the capacity of the 3D memristive arrays. To demonstrate the feasibility of 3D-DPE, we designed and fabricated a CMOS memory controller and monolitically integrated 2 layers of titanium-oxide memristive crossbars. Then we performed the analog dot-product operation under different input conditions in two scenarios: (1) with devices within the same crossbar layer and (2) with devices from different layers. In both cases, the devices exhibited low voltage operation and analog switching behavior with high tuning accuracy.