ESL Modeling for Machine Learning Simulation Speed
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Solution Overview
Problem
Existing ESL design and verification methodologies for machine learning systems face challenges in simulation speed, processing time, and configuration flexibility, particularly when working at the RTL level, leading to increased demands on processing and memory resources.
Innovation Solution
The use of profiling and categorization of machine learning algorithms into fast and slow groups, with slow group operations executed on dedicated hardware and fast group operations supported by conventional semiconductor devices, along with the development of an ESL platform that integrates machine learning hardware and software models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning algorithms are executed at the RTL level, then functionality and verification are improved, but simulation speed and processing efficiency deteriorate
Solution Approach 1:
The patent segments machine learning operations into two distinct groups: fast operations and slow operations. This segmentation allows each group to be executed on appropriately optimized hardware, resolving the contradiction by enabling fast operations to maintain high simulation speed while slow operations achieve thorough verification through dedicated hardware acceleration.
Solution Approach 2:
The patent applies local quality by creating specialized execution environments for different operation types. Fast operations receive optimized software-based execution paths, while slow operations receive dedicated hardware acceleration. This localized optimization resolves the contradiction by tailoring the execution quality and speed to the specific requirements of each operation group.
2Productivity
If machine learning operations are executed on dedicated hardware, then processing efficiency is improved, but device complexity and resource demands increase
Solution Approach 1:
The patent segments the hardware architecture into conventional semiconductor devices for fast operations and dedicated hardware accelerators for slow operations. This segmentation resolves the contradiction by limiting complex dedicated hardware to only those operations that truly require it, while simpler fast operations use conventional devices.
Solution Approach 2:
The patent creates a universal platform that can execute both fast and slow machine learning operations. The system maintains flexibility by allowing conventional devices to handle fast operations while dedicated hardware handles slow operations, resolving the contradiction by making the hardware architecture adaptable to different operation types without requiring entirely separate systems.
3Power
If ESL platform integrates machine learning hardware and software models, then system performance is improved, but development complexity and verification difficulty increase
Solution Approach 1:
The patent segments the ESL platform into distinct hardware and software model components that can be independently developed and verified. This segmentation resolves the contradiction by allowing each component to be optimized separately while maintaining clear interfaces, reducing overall integration complexity despite the sophisticated functionality.
Data Source
AI summary
A non-transitory computer-readable storage medium is encoded with a set of instructions for designing a semiconductor device using electronic system level (ESL) modeling for machine learning applications that, when executed by at least one processor, cause the at least one processor to: retrieve a source code operable to execute a plurality of operations of a machine learning algorithm; classify a first group of the plurality of operations as slow group operations and classify a second group of the plurality of operations as fast group operations, based on a time required to complete each operation; define a neural network operable to execute the slow group operations; define a trained neural network configuration including a plurality of interconnected neurons operable to execute the slow group operations; and generate an ESL platform for evaluating a design of a semiconductor device based on the trained neural network configuration.