Low power and high speed neuromorphic processors have an on-demand need for the growing edge-AI market. Non-Volatile Memory (NVM) based compute-in-memory architecture using Flash memory, STT-MRAM or ReRAM has shown promising results for high energy efficiency compared to the traditional computing architecture. While the various technical challenges such as slow access speed and high fabrication cost exist, radiation-tolerance is the key merit of using emerging nonvolatile memories such as STT-MRAM or ReRAM against Flash memory for space applications.
In this SBIR phase I project, we plan to investigate key reliability architectural challenges, and solutions such that these radiation-tolerant NVMs can be deployed in compute-in-memory based neuromorphic processors for higher performance and energy efficiency compared to the conventional general purpose processors. With this objective, we propose to explore suitable micro-architecture of NVM based compute-in-memory processor, design an NVM based neuromorphic core, and optimize a neural network architecture addressing variation and reliability challenges of the NVM cells along with model quantization.
High speed vision processing from satellites requires massive computing power for running deep neural networks. Emerging nonvolatile memory based neuromorphic processing cores will be able to pave the way for such high speed computing capabilities with suitable radiation-tolerance that the space environment requires.
Edge IoT devices have a growing need of an energy efficient neural network engine to remove energy and latency consuming cloud access. Proposed emerging nonvolatile memory based neuromorphic processing cores will be able to make many edge IoT devices smarter with high energy efficiency compared to conventional general purpose processor cores.