NASA Makes RISC-V the Go-to Ecosystem for Future Space Missions
SiFive is the first company to produce a chip implementing the RISC-V ISA. They've now been selected to provide the core CPU for NASA's next generation High-Performance Spaceflight Computing processor (or HSPC).HPSC is expected to be used in virtually every future space mission, from planetary exploration to lunar and Mars surface missions. HPSC will utilize an 8-core, SiFive(R) Intelligencea X280 RISC-V vector core, as well as four additional SiFive RISC-V cores, to deliver 100x the computational capability of today's space computers. This massive increase in computing performance will help usher in new possibilities for a variety of mission elements such as autonomous rovers, vision processing, space flight, guidance systems, communications, and other applications.... The SiFive X280 is a multi-core capable RISC-V processor with vector extensions and SiFive Intelligence Extensions and is optimized for AI/ML compute at the edge. The X280 is ideal for applications requiring high-throughput, single-thread performance while under significant power constraints. The X280 has demonstrated a 100x increase in compute capabilities compared to today's space computers.. In scientific and space workloads, the X280 provides several orders of magnitude improvement compared to competitive CPU solutions. A business development executive at SiFive says their X280 core "demonstrates orders of magnitude performance gains over competing processor technology," adding that the company's IP "allows NASA to take advantage of the support, flexibility, and long-term viability of the fast-growing global RISC-V ecosystem. "We've always said that with SiFive the future has no limits, and we're excited to see the impact of our innovations extend well beyond our planet." And their announcement stresses that open hardware is a win for everybody:The open and collaborative nature of RISC-V will allow the broad academic and scientific software development community to contribute and develop scientific applications and algorithms, as well optimizing the many math functions, filters, transforms, neural net libraries, and other software libraries, as part of a robust and long-term software ecosystem.
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