A Radiation Hard Network on Chip Neural Processor with RRAM

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.