

Sifive Intelligence XM Series
Overview :
The SiFive Intelligence XM Series is a high-performance AI computing engine launched by SiFive, which delivers exceptional performance-to-power efficiency for compute-intensive applications through the integration of scalar, vector, and matrix engines. This series continues SiFive's tradition of providing efficient memory bandwidth while leveraging the open-source SiFive Kernel Library to accelerate development time.
Target Users :
Target audience includes enterprises and research institutions that require AI workload processing, data flow management, object detection, voice processing, and recommendation handling. The high performance and scalability of the SiFive Intelligence XM Series make it an ideal choice for these applications.
Use Cases
For processing large-scale AI model training and inference tasks.
For efficient data flow management and analysis in data centers.
For real-time object detection and voice processing on edge computing devices.
Features
SiFive Matrix Engine: Designed with Fat Outer Product, tightly integrated with four X-Cores for deep fusion of vector units.
Four X-Cores per cluster: Each equipped with dual vector units to execute all other layers, such as activation functions, with added exponential acceleration instructions.
New matrix instructions: Retrieved by scalar units with source data coming from vector registers, targeting each matrix accumulator.
1 cluster = 16 TOPS (INT8), 8 TFLOPS (BF16) per GHz.
Each XM series cluster provides a sustained bandwidth of 1TB/s.
XM clusters connect to memory in two ways: CHI port for coherent memory access and high-bandwidth port connected to SRAM for storing model data.
Host CPU can be RISC-V, x86, or ARM (or not present).
The system can scale across multiple chips using CHI.
How to Use
1. Visit the SiFive official website and download the SiFive Kernel Library.
2. Select the appropriate XM Series configuration based on project requirements.
3. Integrate the XM Series into the existing hardware or software architecture.
4. Utilize open-source libraries to accelerate the development process.
5. Conduct system testing to ensure performance meets expectations.
6. Expand the system as needed to accommodate larger computational demands.
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