AI BriefingFrom Modules to Agents: An Automated AI Inference Optimization Compiler for 5G RAN
Key point
Developed an AI inference compiler that automatically generates AVX-512 SIMD-optimized code to meet the real-time constraints of 5G RAN.
Details
The adoption of AI models is accelerating in the physical layer (L1) and data link layer (L2) of 5G RAN (Radio Access Network). However, AI models within a 5G base station (gNB) must operate under extremely strict hard real-time constraints. A delay of just 50 microseconds can disrupt the entire communication pipeline.
Existing general-purpose compilers such as TVM or OpenVINO are unsuitable for meeting 5G RAN's tight latency budget. To address this, experts have until now manually written AVX-512 SIMD-optimized C++ kernels, but this approach has the limitation of being difficult to adapt whenever models and hardware change.
To close this gap, Samsung Research developed a hardware-aware AI inference optimization compiler. This compiler automatically converts trained AI models into AVX-512 SIMD-optimized C++ kernels that can be directly integrated into the 5G RAN pipeline.
The compiler technology has evolved through the following two stages.
- Module-based compiler: Analysis, optimization, and code generation modules operate as a deterministic pipeline.
- Multi-agent compiler: LLM-based agents perform the roles previously handled by hardcoded modules.
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