Machine Learning ESL Modeling With Selective Hardware Acceleration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing ESL design and verification methodologies for machine learning systems face challenges in simulation speed, processing time, and limited configuration flexibility, particularly when working at the register-transfer level (RTL), leading to increased demands on processing time and memory resources.
Innovation Solution
Implementing a system and method that utilizes specific algorithms like Bayes classifier, nearest neighbor, decision tree, linear regression, feed forward, and backpropagation to improve simulation speed and efficiency by profiling and categorizing functions into fast and slow groups, mapping them to appropriate hardware and software components, and generating an ESL platform for machine learning systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If machine learning systems are implemented using conventional ESL methodologies at RTL level, then system functionality and verification can be achieved, but simulation speed and processing time deteriorate
Solution Approach 1:
The patent segments machine learning operations into two distinct categories: fast operations that can be executed using conventional software methodologies and slow operations that benefit from dedicated hardware acceleration. This segmentation allows the system to optimize each group differently, applying software flexibility to fast operations and hardware parallelism to slow operations, thereby resolving the contradiction between simulation speed and processing time.
Solution Approach 2:
The patent introduces an intermediary hardware accelerator specifically designed for executing slow machine learning operations. This intermediary component bridges the gap between conventional software-based ESL methodologies and the need for high-speed simulation. The hardware accelerator serves as a mediator that offloads computationally intensive tasks from the main processing system, enabling faster simulation without compromising verification capabilities.
2Productivity
If all machine learning operations are executed using conventional software methodologies, then configuration flexibility is maintained, but processing time and resource consumption increase
Solution Approach 1:
The patent divides machine learning operations into fast and slow groups, allowing different execution strategies for each segment. Fast operations continue to use flexible software implementations, while slow operations are offloaded to dedicated hardware. This segmentation enables the system to achieve high processing efficiency without sacrificing configuration flexibility in the software domain.
Solution Approach 2:
The patent changes the execution parameter for slow machine learning operations from software-based to hardware-based implementation. By altering the execution medium from conventional software methodologies to dedicated hardware accelerators, the system achieves significant improvements in processing efficiency and time performance while maintaining the ability to configure system behavior through software for fast operations.
3Speed
If hardware resources are allocated for all machine learning functions, then simulation speed improves, but device complexity and resource requirements increase
Solution Approach 1:
The patent segments hardware resource allocation by identifying which operations truly require hardware acceleration (slow operations) versus which can be handled by software (fast operations). This selective hardware allocation reduces overall device complexity compared to providing hardware for all functions, while still achieving significant simulation speed improvements for the critical slow operations.
Solution Approach 2:
The patent applies partial hardware acceleration rather than complete hardware implementation for all machine learning functions. By providing hardware resources only for the slow operations that benefit most from acceleration, the system achieves improved simulation speed without the excessive complexity and resource consumption that would result from hardware implementation of all operations.
Data Source
AI summary
A system for designing a semiconductor device, the system including: a first module configured to receive code operable to execute a plurality of operations of a machine learning algorithm and identify, from among the plurality of operations, first operations and second operations that are faster than the first operations, based on a time required to complete each operation; and a second module coupled to the first module to receive information from the first module identifying the first operations, wherein: the second module is configured to define a neural network for executing the first operations; the second module is configured to map the neural network to a machine learning hardware configuration for executing the first operations; and the second module is configured to model the machine learning hardware configuration as one or more semiconductor chips that are useable to execute the first operations.