GSFC Annual Scan Technology Review SpaceCube On-Board Processor Update

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Improved Development Cycle for 8-bit FPGA-Based Soft-Macros Targeting Complex Algorithms

Developing complex algorithms on 8-bit processors without proper development tools is challenging. This paper integrates a series of novel techniques to improve the development cycle for 8-bit soft-macros such as Xilinx PicoBlaze. The improvements proposed in this paper reduce development time significantly by eliminating the required resynthesis of the whole design upon HDL source code changes. Additionally, a technique is proposed to increase the maximum supported data memory size for PicoBlaze which facilitates development of complex algorithms. Also, a general verification technique is proposed based on a series of testbenches that perform code verification using comparison method. The proposed testbench scenario integrates “Inter-Processor Communication (IPC), shared memory, and interrupt” concepts that lays out a guideline for FPGA developers to verify their own designs using the proposed method. The proposed development cycle relies on a chip that has Programmable Logic (PL) fabric (to hold the soft processor) alongside of a hardened processor (to be used as algorithm verifier), therefore, a Xilinx Zynq Ultrascale+ MPSoC is chosen which has a hardened ARM processor. The development cycle proposed in this paper targets the PicoBlaze, but it can be easily ported to other FPGA macros such as Lattice Mico8, or any non-Xilinx FPGA macros

SpaceCube: A NASA Family of Reconfigurable Hybrid On-Board Science Data Processors

SpaceCube is a family of Field Programmable Gate Array (FPGA) based on-board science-data processing systems developed at NASA Goddard Space Flight Center. This presentation provides an overview to the Future In-Space Operations Telecon Working Group

NASA SpaceCube Edge TPU SmallSat Card for Autonomous Operations and Onboard Science-Data Analysis

Using state-of-the-art artificial intelligence (AI)frameworks onboard spacecraft is challenging because common spacecraft processors cannot provide comparable performance to data centers with server-grade CPUs and GPUs available for terrestrial applications and advanced deep-learning networks. This limitation makes small, low-power AI microchip architectures, such as the Google Coral Edge Tensor Processing Unit (TPU), attractive for space missions where the application-specific design enables both high-performance and power-efficient computing for AI applications. To address these challenging considerations for space deployment, this research introduces the design and capabilities of a CubeSat-sized Edge TPU-based co-processor card, known as the SpaceCube Low-power Edge Artificial Intelligence Resilient Node (SC-LEARN). This design conforms to NASA’s CubeSat Card Specification (CS2) for integration into next-generation SmallSat and CubeSat systems. This paper describes the overarching architecture and design of the SC-LEARN, as well as, the supporting test card designed for rapid prototyping and evaluation. The SC-LEARN was developed with three operational modes: (1) a high-performance parallel-processing mode,(2)a fault-tolerant mode for onboard resilience, and (3) a power-saving mode with cold spares. Importantly, this research also elaborates on both training and quantization of TensorFlow models for the SC-LEARN for use onboard with representative, open-source datasets. Lastly, we describe future research plans, including radiation-beam testing and flight demonstration

NASA SpaceCube Next-Generation Artificial-Intelligence Computing for STP-H9-SCENIC on ISS

Recently, Artificial Intelligence (AI) and Machine Learning (ML) capabilities have seen an exponential increase in interest from academia and industry that can be a disruptive, transformative development for future missions. Specifically, AI/ML concepts for edge computing can be integrated into future missions for autonomous operation, constellation missions, and onboard data analysis. However, using commercial AI software frameworks onboard spacecraft is challenging because traditional radiation-hardened processors and common spacecraft processors cannot provide the necessary onboard processing capability to effectively deploy complex AI models. Advantageously, embedded AI microchips being developed for the mobile market demonstrate remarkable capability and follow similar size, weight, and power constraints that could be imposed on a space-based system. Unfortunately, many of these devices have not been qualified for use in space. Therefore, Space Test Program - Houston 9 - SpaceCube Edge-Node Intelligent Collaboration (STP-H9-SCENIC) will demonstrate inflight, cutting-edge AI applications on multiple space-based devices for next-generation onboard intelligence. SCENIC will characterize several embedded AI devices in a relevant space environment and will provide NASA and DoD with flight heritage data and lessons learned for developers seeking to enable AI/ML on future missions. Finally, SCENIC also includes new CubeSat form-factor GPS and SDR cards for guidance and navigation

Implementing a protected zone in a reconfigurable processor for isolated execution of cryptographic algorithms

We design and realize a protected zone inside a reconfigurable and extensible embedded RISC processor for isolated execution of cryptographic algorithms. The protected zone is a collection of processor subsystems such as functional units optimized for high-speed execution of integer operations, a small amount of local memory, and general and special-purpose registers. We outline the principles for secure software implementation of cryptographic algorithms in a processor equipped with the protected zone. We also demonstrate the efficiency and effectiveness of the protected zone by implementing major cryptographic algorithms, namely RSA, elliptic curve cryptography, and AES in the protected zone. In terms of time efficiency, software implementations of these three cryptographic algorithms outperform equivalent software implementations on similar processors reported in the literature. The protected zone is designed in such a modular fashion that it can easily be integrated into any RISC processor; its area overhead is considerably moderate in the sense that it can be used in vast majority of embedded processors. The protected zone can also provide the necessary support to implement TPM functionality within the boundary of a processor