Neural Network Accelerator Debug Circuitry for Breakpoint Control
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Solution Overview
Problem
Debugging hardware accelerators, particularly neural network accelerators, is a time-consuming and complex task due to the lack of dedicated hardware support and the increasing complexity of AI/ML datasets, leading to difficulties in identifying bugs, performance bottlenecks, and erroneous output generation.
Innovation Solution
The implementation of debug circuitry that can halt execution at specified breakpoints, single-step through output transactions, and analyze data generation to isolate performance issues and errors, reducing the need for software-based debugging and custom modifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If dedicated hardware debugging support is added to hardware accelerators, then debugging capability and reliability are improved, but device complexity increases
Solution Approach 1:
The patent combines the hardware accelerator cores with integrated debug circuitry, merging the computing and debugging functions into a single unified hardware structure. This integration allows the accelerator to perform both its primary computational task and self-diagnosis simultaneously, improving reliability without requiring separate external debugging equipment.
Solution Approach 2:
The patent introduces a debug circuit as an intermediary component between the accelerator cores and external debugging systems. This debug circuit includes a queue mechanism that mediates communication between cores and external tools, enabling structured debug information transfer while maintaining the complexity of the accelerator architecture.
2Loss of time
If software-based debugging methods are used without dedicated hardware support, then device complexity is kept low, but debugging time and effort increase significantly
Solution Approach 1:
The patent implements preliminary actions by pre-configuring debug queues and breakpoints within the hardware accelerator before execution occurs. The debug circuitry is prepared in advance to capture specific events and states, allowing rapid diagnosis without requiring complex real-time software intervention or post-hoc analysis.
Solution Approach 2:
The hardware accelerator performs self-service debugging through its integrated debug circuitry that automatically captures errors, generates debug information, and interfaces with external tools. This self-diagnosis capability eliminates the need for extensive manual software-based debugging procedures, significantly reducing debugging time while maintaining acceptable hardware complexity.
3Productivity
If hardware accelerators process complex AI/ML datasets, then computational capability is improved, but difficulty in detecting and measuring errors increases
Solution Approach 1:
The patent segments the hardware accelerator into multiple independent cores, each capable of processing specific portions of complex AI/ML datasets. The debug circuitry is correspondingly segmented to monitor each core independently, allowing error detection in parallel without overwhelming the debugging system. This segmentation maintains high computational capability while making error detection manageable through distributed monitoring.
Data Source
AI summary
Methods, apparatus, systems, and articles of manufacture are disclosed to debug a hardware accelerator such as a neural network accelerator for executing Artificial Intelligence computational workloads. An example apparatus includes a core with a core input and a core output to execute executable code based on a machine-learning model to generate a data output based on a data input, and debug circuitry coupled to the core. The debug circuitry is configured to detect a breakpoint associated with the machine-learning model, compile executable code based on at least one of the machine-learning model or the breakpoint. In response to the triggering of the breakpoint, the debug circuitry is to stop the execution of the executable code and output data such as the data input, data output and the breakpoint for debugging the hardware accelerator.