Composable Machine Learning Compute Nodes for Hardware Software Co-Design
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Solution Overview
Problem
Existing Automated Machine Learning (AutoML) approaches lack the flexibility in hardware and software design, restricting the exploration of efficient AI/ML hardware and software co-designs, as they typically use fixed hardware and software templates, limiting the discovery of optimal models for specific workloads.
Innovation Solution
The introduction of composable machine learning compute nodes that incorporate hardware and software heterogeneity, allowing for dynamic modulation of architecture templates based on micro-architectural parameters, enabling the exploration of a richer space of HW/SW designs across multiple architecture styles through an expressive search space representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If fixed hardware and software templates are used in AutoML approaches, then system simplicity is maintained, but design flexibility and ability to explore optimal configurations are restricted
Solution Approach 1:
The system segments the hardware and software configuration space into discrete, composable building blocks (hardware templates and software templates). Each template represents a modular unit that can be independently selected and combined, enabling flexible exploration of design spaces without requiring a monolithic complex system.
Solution Approach 2:
The system dynamically modulates architecture templates based on micro-architectural parameters and workload characteristics. Rather than using fixed configurations, the system can adaptively adjust hardware and software templates to optimize performance for specific workloads, transforming static templates into dynamic configurable structures.
2Adaptability or versatility
If composable machine learning compute nodes with hardware and software heterogeneity are introduced, then design flexibility and exploration capability are improved, but system complexity increases
Solution Approach 1:
The system employs universal template structures that can serve multiple functions across different workloads. Hardware templates and software templates are designed as multi-functional building blocks that can be reused and recombined for various machine learning tasks, reducing the need for specialized complex systems for each specific function.
Solution Approach 2:
The system manages complexity by parameterizing templates rather than creating entirely new structures. By modulating architecture templates through parameter adjustments (micro-architectural parameters), the system achieves high exploration capability while reusing the same template frameworks, thereby controlling system complexity.
3Manufacturing precision
If dynamic modulation of architecture templates is enabled, then optimal configurations for specific workloads can be discovered, but computational overhead and development effort increase
Solution Approach 1:
The system performs preliminary action by pre-defining hardware and software templates with common configurations and patterns. These templates are prepared in advance with typical micro-architectural parameters, so that when a specific workload needs optimization, the system only needs to select and adjust from pre-prepared templates rather than creating configurations from scratch, reducing development effort.
Solution Approach 2:
The system uses copying by replicating successful template configurations across different workloads. Once an optimal configuration is discovered for one workload, the same template structure can be copied and adapted for similar workloads, reducing the computational overhead and development effort required for each individual optimization task.
Data Source
AI summary
Methods, apparatus, systems, and articles of manufacture are disclosed for composable machine learning compute nodes. An example apparatus includes interface circuitry to receive a workload, instructions in the apparatus, and processor circuitry to at least one of execute or instantiate the instructions to generate a first configuration of one or more machine-learning models based on a workload, generate a second configuration of hardware, determine an evaluation parameter based on an execution of the workload, the execution of the workload based on the first configuration and the second configuration, and, in response to the evaluation parameter satisfying a threshold, execute the one or more machine-learning models in the first configuration on the hardware in the second configuration, the one or more machine-learning models and the hardware to execute the workload.