AI 용어집
인공지능 완전 사전
GPU Computing for AI
Use of graphics processors (GPU) to massively parallelize and accelerate matrix computations for AI.
TPU and Specialized Accelerators
Processors specifically designed for AI workloads such as Google's TPUs and other dedicated ASICs.
Distributed Computing for AI
Distribution of training tasks across multiple machines/nodes for horizontal scalability.
Model Parallelism
Splitting a large neural model across multiple accelerators to overcome memory limitations.
HPC Memory Optimization
Advanced memory management techniques to minimize transfers and maximize cache utilization.
Quantization and Compression
Reducing the numerical precision of weights and activations to speed up computation.
Mixed Precision Computing
Simultaneous use of different precisions (FP32, FP16, INT8) to optimize performance/accuracy.
FPGA for AI Acceleration
Programming of reconfigurable logic circuits to implement custom AI operations.
Parallel Processing Pipeline
Orchestration of computational steps in a pipeline to maximize hardware resource utilization.
Edge Computing HPC
High-performance computing deployment optimized for edge device constraints.
Code Compilation and Optimization
Use of specialized compilers (TVM, XLA) to optimize machine code for specific architectures.
Quantum Computing for AI
Exploring quantum architectures to solve certain AI problems more efficiently.
HPC-AI Systems Architecture
Design of optimized hardware infrastructures interconnecting CPUs, GPUs, memory, and networks.
Optimization of Data Transfers
Minimization of bottlenecks related to transfers between CPU, GPU, and storage.
Optimized Dynamic Batching
Automatic batch size adaptation to maximize throughput on available hardware.