Under this effort, Niobium Microsystems, Inc. is proposing a low power computing architecture accelerator for neuromorphic processing which can enable real-time sensor data processing and autonomous decision making that is cost-effective and scalable to the growing data ingestion and processing needs of future autonomous systems. The proposed architecture will be highly scalable and compatible with modern processor systems (such as RISC-V or ARM), so that it can be easily adopted in a variety of new systems, and also easily integrated into existing systems. Additionally, Niobium proposes to integrate the proposed accelerator into a larger SoC that will serve as a proving ground and reference design for the accelerator concept. The SoC will be capable of acting as a primary processor in systems or as a co-processor to existing systems. Ultimately Niobium intended to utilize this accelerator as a standard block in its family of heterogeneous processor architectures.
Niobium proposes the following four technical objectives for Phase I:
(1) Study prior efforts and capture the performance and efficiency metrics as well as the limitations of existing platforms;
(2) Propose a novel architecture for a neuromorphic accelerator compatible with heterogeneous processor platforms (RISC-V- or ARM-based);
(3) Explore available MRAM technology (GlobalFoundries 22FDX), characterize its PPA and propose ways for incorporating into the architecture; and
(4) Estimate performance, power and efficiency metrics for comparison to existing solutions.
Space platform which require on-board energy efficient inference capabilities and possibly decision making and action will benefit from the low-power energy efficient inference capability of Neuromorphic processors. Long range missions that will require long-term unsupervised learning and adaptation based on constantly evolving unpredictable conditions can also benefit by the learning modalities that Neuromorphic architectures uniquely support.
Niobium is pursuing a fabless semiconductor model & planning to incorporate this accelerator into future energy-efficient SoCs along with existing accelerators for DNNs, cryptography & other computationally intensive functions. These energy-efficient processor SoCs will target energy-constrained application markets (unsupervised sensors & sensor networks, lightweight robotics, drones, wearables).