Multi-core processor

Statistics

Operating system

Training (meteorology)

Telecommunications

Scalability

Parallel computing

Core (optical fiber)

Computer science

Modular design

Physics

Computer architecture

Mathematics

SIMD

Enhanced Data Rates for GSM Evolution

Inference

Supercomputer

Microarchitecture

Artificial intelligence

Meteorology

Inference engine

Flexibility (engineering)

Artificial neural network

A processor core is presented for AI training and inference products. Leading-edge compute efficiency is achieved for robust fp16 training via efficient heterogeneous 2-D systolic array-SIMD compute engines leveraging compact DLFloat16 FPUs. Architectural flexibility is maintained for very high compute utilization across neural network topologies. A modular dual-corelet architecture with a shared scratchpad and a software-controlled network/memory interface enables scalability to many-core SoCs and large-scale systems. The 14nm AI core achieves fp16 peak performance of 3.0 TFLOPS at 0.62V and 1.4 TFLOPS/W at 0.54V.

A 3.0 TFLOPS 0.62V Scalable Processor Core for High Compute Utilization AI Training and Inference