Distributed Depth Trace Receiver for Debugging
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Solution Overview
Problem
Current processor and memory emulation technologies face challenges in accurately modeling cache events and system interactions, leading to performance discrepancies between simulated and actual target performance, especially with cache-based architectures, which affects developer confidence and debugging efficiency.
Innovation Solution
A trace architecture with advanced compression techniques, adaptive calibration methods, and multi-input trace receivers is implemented to enhance debug and profiling capabilities, providing better visibility into system and memory behavior, and addressing usability issues such as stall cycle profiling, event profiling, and cache coherence analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single trace receiver is used, then device complexity is reduced, but the amount of trace data that can be processed is limited
Solution Approach 1:
The trace receiver is divided into multiple independent input units (first trace input unit, second trace input unit, etc.), each capable of receiving and processing trace data separately. This segmentation allows the system to handle larger volumes of trace data while keeping each individual unit relatively simple in structure.
2Quantity of substance
If multiple input units are connected in series, then trace data processing capacity increases, but device complexity increases
Solution Approach 1:
Multiple trace input units are connected in a series configuration where the output of one unit feeds into the next, creating a cascaded processing system. This merging approach allows trace data to flow through multiple processing stages, increasing overall processing capacity while maintaining a systematic and organized structure that manages complexity.
3Measurement precision
If trace data is collected without compression, then measurement precision is maintained, but loss of information increases due to bandwidth limitations
Solution Approach 1:
The patent extracts and removes redundant or less critical information from the trace data stream during processing. By identifying and eliminating unnecessary data elements, the system maintains the precision of critical measurements while reducing the overall data volume that needs to be transmitted, thereby preventing information loss due to bandwidth constraints.
Data Source
AI summary
Input processing limitations may be solved by placing multiple units in series, with each unit recording some portion of the incoming data. This requires the generation of simultaneous actions across units operating in series, with both the data recording and user command execution happening at the same point in the trace data stream.