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The growing adoption of RISC-V in high-performance and scientific computing has increased the need for performance-portable code targeting the RISC-V Vector (RVV) extension. However, current compiler infrastructures provide limited end-to-end support for generating optimized RVV code from high-level representations to low-level implementations. In particular, existing MLIR distributions lack practical lowering paths that map high-level abstractions to RVV intrinsics, limiting their applicability for production-ready RISC-V kernels. This paper presents a compilation approach that combines MLIR with xDSL to bridge the missing lowering stages required for RVV code generation.
As AI workloads scale exponentially, bandwidth has become the new bottleneck. This article explores how Celestial AI’s Photonic Fabric™ redefines interconnect performance, and how Sofics’ low-cap ESD and power clamp IP enabled its integration on TSMC’s 5nm platform.
See how Codasip Studio enables SYCLOPS to implement a customizable RISC-V Vector processor, accelerating the future of open-source, high-performance AI compute at the Edge.
The era of universal processor architectures is giving way to workload-specific designs optimized for performance, power, and scalability. As data-centric applications in artificial intelligence (AI), edge computing, automotive, and industrial markets continue to expand, they are driving a fundamental shift in processor design.
The authors introduce a HW/SW co-design toolset capable of adapting to a user-defined architecture description that captures the instruction set extension semantics.
When designing complex Systems-on-Chip, software teams should be involved early in the process. This will help identify architectural bottlenecks, validate system behavior, and accelerate the entire project timeline. In this process, processor simulation models play a crucial role.
