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Register a new userEffective Pre-Silicon Verification of Processor Cores by Breaking the Bounds of Symbolic Quick Error Detection
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Added by
Subhasish Mitra
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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We present a novel approach to pre-silicon verification of processor designs. The purpose of pre-silicon verification is to find logic bugs in a design at an early stage and thus avoid time- and cost-intensive post-silicon debugging. Our approach relies on symbolic quick error detection (Symbolic QED, or SQED). SQED is targeted at finding logic bugs in a symbolic representation of a design by combining bounded model checking (BMC) with QED tests. QED tests are powerful in generating short sequences of instructions (traces) that trigger bugs. We extend an existing SQED approach with symbolic starting states. This way, we enable the BMC tool to select starting states arbitrarily when generating a trace. To avoid false positives, (e.g., traces starting in unreachable states that may not be-have in accordance with the processor instruction-set architecture), we define constraints to restrict the set of possible starting states. We demonstrate that these constraints, togeth-er with reasonable assumptions about the system behavior, allow us to avoid false positives. Using our approach, we discovered previously unknown bugs in open-source RISC-V processor cores that existing methods cannot detect. Moreover, our novel approach out-performs existing ones in the detection of bugs having long traces and in the detection of hardware Trojans, i.e., unauthorized modifications of a design.
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We present an industrial case study that demonstrates the practicality and effectiveness of Symbolic Quick Error Detection (Symbolic QED) in detecting logic design flaws (logic bugs) during pre-silicon verification. Our study focuses on several microcontroller core designs (~1,800 flip-flops, ~70,000 logic gates) that have been extensively verified using an industrial verification flow and used for various commercial automotive products. The results of our study are as follows: 1. Symbolic QED detected all logic bugs in the designs that were detected by the industrial verification flow (which includes various flavors of simulation-based verification and formal verification). 2. Symbolic QED detected additional logic bugs that were not recorded as detected by the industrial verification flow. (These additional bugs were also perhaps detected by the industrial verification flow.) 3. Symbolic QED enables significant design productivity improvements: (a) 8X improved (i.e., reduced) verification effort for a new design (8 person-weeks for Symbolic QED vs. 17 person-months using the industrial verification flow). (b) 60X improved verification effort for subsequent designs (2 person-days for Symbolic QED vs. 4-7 person-months using the industrial verification flow). (c) Quick bug detection (runtime of 20 seconds or less), together with short counterexamples (10 or fewer instructions) for quick debug, using Symbolic QED.
Symbolic quick error detection (SQED) is a formal pre-silicon verification technique targeted at processor designs. It leverages bounded model checking (BMC) to check a design for counterexamples to a self-consistency property: given the instruction set architecture (ISA) of the design, executing an instruction sequence twice on the same inputs must always produce the same outputs. Self-consistency is a universal, implementation-independent property. Consequently, in contrast to traditional verification approaches that use implementation-specific assertions (often generated manually), SQED does not require a full formal design specification or manually-written properties. Case studies have shown that SQED is effective for commercial designs and that SQED substantially improves design productivity. However, until now there has been no formal characterization of its bug-finding capabilities. We aim to close this gap by laying a formal foundation for SQED. We use a transition-system processor model and define the notion of a bug using an abstract specification relation. We prove the soundness of SQED, i.e., that any bug reported by SQED is in fact a real bug in the processor. Importantly, this result holds regardless of what the actual specification relation is. We next describe conditions under which SQED is complete, that is, what kinds of bugs it is guaranteed to find. We show that for a large class of bugs, SQED can always find a trace exhibiting the bug. Ultimately, we prove full completeness of a variant of SQED that uses specialized state reset instructions. Our results enable a rigorous understanding of SQED and its bug-finding capabilities and give insights on how to optimize implementations of SQED in practice.
We present Symbolic Quick Error Detection (Symbolic QED), a structured approach for logic bug detection and localization which can be used both during pre-silicon design verification as well as post-silicon validation and debug. This new methodology leverages prior work on Quick Error Detection (QED) which has been demonstrated to drastically reduce the latency, in terms of the number of clock cycles, of error detection following the activation of a logic (or electrical) bug. QED works through software transformations, including redundant execution and control flow checking, of the applied tests. Symbolic QED combines these error-detecting QED transformations with bounded model checking-based formal analysis to generate minimal-length bug activation traces that detect and localize any logic bugs in the design. We demonstrate the practicality and effectiveness of Symbolic QED using the OpenSPARC T2, a 500-million-transistor open-source multicore System-on-Chip (SoC) design, and using difficult logic bug scenarios observed in various state-of-the-art commercial multicore SoCs. Our results show that Symbolic QED: (i) is fully automatic, unlike manual techniques in use today that can be extremely time-consuming and expensive; (ii) requires only a few hours in contrast to manual approaches that might take days (or even months) or formal techniques that often take days or fail completely for large designs; and (iii) generates counter-examples (for activating and detecting logic bugs) that are up to 6 orders of magnitude shorter than those produced by traditional techniques. Significantly, this new approach does not require any additional hardware.
Field programmable gate arrays (FPGAs) provide designers with the ability to quickly create hardware circuits. Increases in FPGA configurable logic capacity and decreasing FPGA costs have enabled designers to more readily incorporate FPGAs in their designs. FPGA vendors have begun providing configurable soft processor cores that can be synthesized onto their FPGA products. While FPGAs with soft processor cores provide designers with increased flexibility, such processors typically have degraded performance and energy consumption compared to hard-core processors. Previously, we proposed warp processing, a technique capable of optimizing a software application by dynamically and transparently re-implementing critical software kernels as custom circuits in on-chip configurable logic. In this paper, we study the potential of a MicroBlaze soft-core based warp processing system to eliminate the performance and energy overhead of a soft-core processor compared to a hard-core processor. We demonstrate that the soft-core based warp processor achieves average speedups of 5.8 and energy reductions of 57% compared to the soft core alone. Our data shows that a soft-core based warp processor yields performance and energy consumption competitive with existing hard-core processors, thus expanding the usefulness of soft processor cores on FPGAs to a broader range of applications.
During post-silicon validation, manufactured integrated circuits are extensively tested in actual system environments to detect design bugs. Bug localization involves identification of a bug trace (a sequence of inputs that activates and detects the bug) and a hardware design block where the bug is located. Existing bug localization practices during post-silicon validation are mostly manual and ad hoc, and, hence, extremely expensive and time consuming. This is particularly true for subtle electrical bugs caused by unexpected interactions between a design and its electrical state. We present E-QED, a new approach that automatically localizes electrical bugs during post-silicon validation. Our results on the OpenSPARC T2, an open-source 500-million-transistor multicore chip design, demonstrate the effectiveness and practicality of E-QED: starting with a failed post-silicon test, in a few hours (9 hours on average) we can automatically narrow the location of the bug to (the fan-in logic cone of) a handful of candidate flip-flops (18 flip-flops on average for a design with ~ 1 Million flip-flops) and also obtain the corresponding bug trace. The area impact of E-QED is ~2.5%. In contrast, deter-mining this same information might take weeks (or even months) of mostly manual work using traditional approaches.
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