ANVMP: A 28nm 52.6 μW 1.25pJ/SOP Asynchronous Non- Volatile-Memory-based Computing-In-Memory Neuromorphic Processor for Edge-Al Applications

The increasing demand for low power edge-Al devices that target various tasks has driven many research efforts. To address this demand, many technologies such as computing-in-memory (CIM) [1], spiking neural networks (SNN) [2], and asynchronous logic [3] have been implemented on edge-Al devices. However, previous works either had energy consumptions of many mW [1, 2, 3, 5] or exclusively addressed a single task [4]. In this paper, we propose a neuromorphic processor (ANVMP) that adapts non-volatile memory (NVM)-based CIM, asynchronous logic and achieves the performance of $52.6~ \mu \text{W} 1.25 \text{pJ} / \text{SOP} \text{@} 0.55 ~\text{V}, 10 \%$ event rate, and $0.7 \%$ activation ratio (Fig. 1 (top)). The energy-efficient NVM-based CIM allows to power off the CIM macros completely during idle time to save power. The SNN-based neuromorphic computing uses binary coding which removes the need for DACs for CIM processing, and sparse spikes which reduce the energy consumption of information transmission with the cost of throughput. The event-driven nature of asynchronous logic allows energy to go where and when needed. Moreover, asynchronous logic offers fine-grained controls of CIM macros, which saves its power-on time.