Overview
ISO/IEC TR 17903:2024 - "Information technology - Artificial intelligence - Overview of machine learning computing devices" is a Technical Report that surveys terminology, representative characteristics and existing approaches for optimizing performance of machine learning (ML) computing devices. Published as a first edition in 2024 by ISO/IEC JTC 1/SC 42, the report is an informational resource intended to help AI stakeholders understand the building blocks and performance factors of ML computing devices. It is relevant to organizations of all sizes and does not prescribe mandatory requirements.
Key topics
The report organizes content across conceptual, technical and optimization topics:
- Concepts and terminology: ML computing, AI computing, computing device, infrastructure, functional units and services (Clause 3–5).
- ML computing device characteristics (Clause 6), including:
- Datatypes and their impact on effectiveness and efficiency
- ML operators (primitives used by models)
- Memory access and addressing mechanisms
- Scheduling and job orchestration mechanisms
- Topologies (interconnects and device layouts)
- Streams, buffering and cache mechanisms
- Data exchange and memory interoperability mechanisms
- Performance optimization approaches (Clause 7):
- Computing resource–level tactics (hardware configuration, interconnects, memory usage)
- Enabling software–level tactics (runtime, compiler and scheduling strategies)
- Measures and metrics for evaluating device performance
Practical applications
This Technical Report helps stakeholders make practical decisions around ML computing devices:
- Hardware architects and vendors - understand device characteristics important for ML workloads and how design choices affect efficiency.
- System integrators and IT managers - select and configure accelerators, memory, and interconnects to meet performance and cost targets.
- ML engineers and platform teams - tune datatypes, operators and scheduling to improve model training and inference throughput and latency.
- Procurement and operations - evaluate compatibility, scalability and interoperability of ML computing devices across infrastructures.
- Researchers and standards developers - build on a common vocabulary and survey of current optimization approaches.
By clarifying ML computing device terminology and surveying characteristic-setting approaches, ISO/IEC TR 17903:2024 supports better alignment between ML workloads, device capabilities and performance optimization strategies.
Related standards
These documents are cited as normative references and provide complementary guidance on AI/ML terminology and system-level frameworks.
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