Description: Green Mountain Semiconductor Inc. (GMS) is proposing an in-memory solution utilizing non-volatile memory (NVM) in FD-SOI technology for the solicitation H6.22-1123. While rethinking the fundamental architecture of neural networks based on the radiation hard criteria, GMS has developed a robust CIM architecture, incorporating non-volatile RAM with radiation hard characteristics for low-power and low-latency AI edge devices. Existing state of the art solutions utilize separate chips for weight storage and require large amounts of on-chip SRAM in order to load weight information and process data. While off-chip weight storage does allow for various types of memory to be used based on user application, the penalty comes in the form of weight transfer on-chip, which is not generally accounted for in the power numbers and efficiency touted by neural network chip designers. In order to limit weight movement power cost, neural networks store large amounts of data in SRAM in order to perform large batch operations. This, in itself, introduces another costly weak point with regards to space applications, that being the storage of information for extended periods of time in SRAM memory cells. GMS has been able to develop a CIM architecture that eliminates the von Neumann bottleneck by integrating all memory for weight storage on chip, significantly reducing weight movement and decreasing the total amount of SRAM, as well as the length of time that data is stored in SRAM. This unique design allows for accelerated AI inferencing, particularly for Convolutional Neural Networks (CNN), outperforming other state of the art architectures. Focusing on SRAM reduction coupled with circuit design techniques, using radiation hard NVM and radiation hardened logic the chip reaches radiation target levels required for deep space computing.

Benefits: Potential NASA applications include any critical image processing mission where device functionality is imperative. This includes deep space missions subject to solar flares. Flight navigation is of particular interest in this respect. Habitable Worlds Observatory would be a prime candidate, requiring long life, low power, and edge computing capabilities. Lunar missions can also benefit from the innovation's low power, low leakage, and power-down, instant-on capabilities, especially when operating in extended critically low power conditions.

Non-Nasa applications may include a multitude of terrestrial and near space applications: Robots working in environments with elevated radiation levels (nuclear power plants, cleanup operations following nuclear accidents or acts of warfare) • Life-critical systems such as operating room equipment and implanted medical devices • Sensitive and safety-critical automotive controls • Aerospace electronics