Identifying post-silicon bugs and their root causes through a hardware introspection engine
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The goal of this project is to design, build, and evaluate new hardware mechanisms to debug post-silicon bugs in Systems-on-Chip (SoCs). Specifically, we aim to accelerate the diagnosis of complex bugs such as deadlocks that are notoriously hard to identify using existing debugging mechanisms such as ARM CoreSight and hardware performance counters. We will design and evaluate programmable introspection mechanisms that will analyze streams of program and hardware-level trace data at test- and run-time, check correctness invariants, and generate event summaries that point to root causes of bugs. This thesis describes an on-chip hardware introspection engine (HIE) that detects anomalous transactions and alerts the user of potential bugs that could lead to deadlock. The HIE is a device that attaches to a bus and snoops on request and response transactions and collects response latency metadata for the transactions it receives. From this metadata, HIE is able to evaluate the normal behavior of transactions and alert engineers when anomalous behavior is detected at run-time. The HIE also separates the metadata it collects for different address ranges, creating a local version of the memory map that allows easy integration into existing systems. Synthesis on a FPGA and simulation of the HIE show that minimal area overhead is required for implementation and 100% detection accuracy is achievable for deadlock scenarios. The concept of learning address ranges and collecting and analyzing metadata for these ranges can have many applications in different fields that leverage anomaly detection, i.e. security, debug, etc.