This paper presents the Quick Error Detection (QED) technique for systematically creating families of post-silicon validation tests that quickly detect bugs inside processor cores and uncore components (cache controllers, memory controllers, and on-chip interconnection networks) of multicore system on chips (SoCs). Such quick detection is essential because long error detection latency, the time elapsed between the occurrence of an error due to a bug and its manifestation as an observable failure, severely limits the effectiveness of traditional post-silicon validation approaches. QED can be implemented completely in software, without any hardware modification. Hence, it is readily applicable to existing designs. Results using multiple hardware platforms, including the Intel® Core™ i7 SoC, and a state-of-the-art commercial multicore SoC, along with simulation results using an OpenSPARC T2-like multicore SoC with bug scenarios from commercial multicore SoCs demonstrate: 1) error detection latencies of post-silicon validation tests can be very long, up to billions of clock cycles, especially for bugs inside uncore components; 2) QED shortens error detection latencies by up to nine orders of magnitude to only a few hundred cycles for most bug scenarios; and 3) QED enables up to a fourfold increase in bug coverage.

Existing post-silicon validation techniques are generally ad hoc, and their cost and complexity are rising faster than design cost. Hence, systematic approaches to post-silicon validation are essential. Our research indicates that many of the bottlenecks of existing post-silicon validation approaches are direct consequences of very long error detection latencies. Error detection latency is the time elapsed between the activation of a bug during post-silicon validation and its detection or manifestation as a system failure. In our earlier papers, we created the Quick Error Detection (QED) technique to overcome this significant challenge. QED systematically creates a wide variety of post-silicon validation tests to detect bugs in processor cores and uncore components of multi-core System-on-Chips (SoCs) very quickly, i.e., with very short error detection latencies. In this paper, we present an overview of QED and summarize key results: 1. Error detection latencies of “typical” post-silicon validation tests can range up to billions of clock cycles. 2. QED shortens error detection latencies by up to 6 orders of magnitude. 3. QED enables 2- to 4-fold improvement in bug coverage. QED does not require any hardware modification. Hence, it is readily applicable to existing designs.

We present a new technique for systematically creating postsilicon validation tests that quickly detect bugs in processor cores and uncore components (cache controllers, memory controllers, on-chip networks) of multi-core System on Chips (SoCs). Such quick detection is essential because long error detection latency, the time elapsed between the occurrence of an error due to a bug and its manifestation as an observable failure, severely limits the effectiveness of existing post-silicon validation approaches. In addition, we provide a list of realistic bug scenarios abstracted from "difficult" bugs that occurred in commercial multi-core SoCs. Our results for an OpenSPARC T2-like multi-core SoC demonstrate: 1. Error detection latencies of "typical" post-silicon validation tests can be very long, up to billions of clock cycles, especially for bugs in uncore components. 2. Our new technique shortens error detection latencies by several orders of magnitude to only a few hundred cycles for most bug scenarios. 3. Our new technique enables 2-fold increase in bug coverage. An important feature of our technique is its software-only implementation without any hardware modification. Hence, it is readily applicable to existing designs.

Long error detection latency, the time elapsed between the occurrence of an error caused by a bug and its manifestation as a system-level failure, is a major challenge in post-silicon validation of robust systems. In this paper, we present a new technique called Quick Error Detection (QED), which transforms existing post-silicon validation tests into new validation tests that significantly reduce error detection latency. QED transformations allow flexible tradeoffs between error detection latency, coverage, and complexity, and can be implemented in software with little or no hardware changes. Results obtained from hardware experiments on quad-core Intel®Core™ i7 hardware platforms and from simulations on a multi-core MIPS processor design demonstrate that: 1. QED significantly improves error detection latencies by six orders of magnitude, i.e., from billions of cycles to a few thousand cycles or less. 2. QED transformations do not degrade the coverage of validation tests as estimated empirically by measuring the maximum operating frequencies over a wide range of operating voltage points. 3. QED tests improve coverage by detecting errors that escape the original non-QED tests.

Instruction Footprint Recording and Analysis (IFRA) overcomes challenges associated with an expensive step in post-silicon validation of processors-pinpointing the bug location and the instruction sequence that exposes the bug from a system failure. On-chip recorders collect instruction footprints (information about flows of instructions and what the instructions did as they passed through various design blocks) during the normal operation of the processor in a post-silicon system validation setup. Upon system failure, the recorded information is scanned out and analyzed offline for bug localization. Special self-consistency-based program analysis techniques, together with the test program binary of the application executed during post-silicon validation, are used for this purpose. Major benefits of using IFRA over traditional techniques for post-silicon bug localization are as follows: 1) it does not require full system-level reproduction of bugs, and 2) it does not require full system-level simulation. Simulation results on a complex superscalar processor demonstrate that IFRA is effective in accurately localizing electrical bugs with very little impact on overall chip area.