With technology scaling leading to reliability problems and a proliferation of hardware accelerators, there is a need for cost-effective techniques to detect errors in complex datapaths. Modulo (residue) arithmetic is useful for creating a shadow datapath to check the computation of an arithmetic datapath and involves three key steps: 1) reduction of the inputs to modulo shadow values; 2) computation with those shadow values; and 3) checking the outputs for consistency with the shadow outputs. The focus of this paper is new gate-level architectures and algorithms to reduce the cost of modulo shadow datapaths. We introduce new low-cost architectures for the functional units performing the aforementioned reduction, shadow computation, and checking operations. We compare our functional units to the previous state-of-the-art approach, observing a 12.5% reduction in area and a 47.1% reduction in delay for a 32-bit mod-3 reducer; that our reducer costs, which tend to dominate shadow datapath costs, do not increase with larger modulo bases; and that for modulo-15 and above, all of our functional units have better area and delay than their previous counterparts. To demonstrate the cost-effectiveness of our approach in computation-intensive accelerator applications, we design custom pipelined shadow datapaths for five compound functional units implementing a variety of vector and matrix operations. For a 32-bit main datapath and 2-bit shadow datapath, we observe area costs of 6%-10% and reliability improvements against single event transient errors of 3-61×. For an 8-bit shadow datapath, we observe area costs of 15%-20% and reliability gains of 121-2477×.

Validation and debug challenges of system-on-chips (SoCs) are getting increasingly difficult. As we reach the limits of Dennard scaling, efforts to improve system performance and energy efficiency have resulted in the integration of a wide variety of complex hardware accelerators in SoCs. Hence, it is essential to address the validation and debug of hardware accelerators. High-level synthesis (HLS) is a promising technique to rapidly create customized hardware accelerators. In this paper, we present the hybrid quick error detection (H-QED) approach that overcomes validation and debug challenges for hardware accelerators by leveraging HLS techniques in both the presilicon and post-silicon stages. H-QED improves error detection latencies (time elapsed from when a bug is activated to when it is detected) by 2-5 orders of magnitude with one cycle latencies in presilicon scenarios and bug coverage threefold higher compared to traditional validation techniques. H-QED also uncovered previously unknown bugs in the CHStone benchmark suite, which is widely used by the HLS community. H-QED incurs an 8% accelerator area overhead with negligible silicon performance impact for post-silicon stage, and we also introduce techniques to minimize any possible intrusiveness introduced by H-QED.

With technology scaling leading to reliability problems and a proliferation of hardware accelerators, there is a need for cost-effective techniques to detect errors in complex datapaths. Modulo (residue) arithmetic is useful for creating a shadow datapath to check the computation of an arithmetic datapath and involves three key steps: reduction of the inputs to modulo shadow inputs, computation with those shadow values, and checking the outputs for consistency with the shadow outputs. The focus of this paper is new gate-level architectures and algorithms to reduce the cost of modulo shadow datapaths. We introduce low-cost architectures for all four key functional units in a shadow datapath: (1) a modulo reduction algorithm that generates architectures consisting entirely of full-adder standard cells; (2) minimum-area modulo adder and subtractor architectures; (3) an array-based modulo multiplier design; and (4) a modulo equality comparator that handles the residue encoding produced by the above. We compare our functional units to the previous state-of-the-art approach, observing a 12.5% reduction in area and a 47.1% reduction in delay for a 32-bit mod-3 reducer; that our reducer costs, which tend to dominate shadow datapath costs, do not increase with larger modulo bases; and that for modulo-15 and above, all of our modulo functional units have better area and delay then their previous counterparts. We also demonstrate the practicality of our approach by designing a custom shadow datapath for error detection of a multiply accumulate functional unit, which has an area overhead of only 12% for a 32-bit main datapath and 2-bit modulo-3 shadow datapath.

CLEAR (Cross-Layer Exploration for Architecting Resilience) is a first of its kind framework which overcomes a major challenge in the design of digital systems that are resilient to hardware errors: achieve desired resilience targets at low cost (energy, power, execution time, area) by combining resilience techniques across various layers of the system stack (circuit, logic, architecture, software, algorithm). CLEAR automatically and systematically explores the large space of resilience techniques and their combinations, derives cost-effective solutions, provides guidelines for designing new techniques, and offers insights into how to design cost-effective digital systems resilient to hardware errors: 1. circuit-level techniques are crucial; 2. application-level guidance is essential; 3. existing architecture and software techniques are generally expensive or provide too little resilience; 4. some previously published techniques suffer from inaccurate analysis, leading to incorrect conclusions; 5. cost-effective protection from multiple error sources is achieved by combining techniques targeting each specific error source.

High-level synthesis (HLS) promises high-quality hardware with minimal development effort. In this paper, we evaluate the current state-of-the-art in HLS and design techniques based on software references and architecture references. We present a software reference study developing a JPEG encoder from pre-existing software, and an architecture reference study developing an AES block encryption module from scratch in SystemC and SystemVerilog based on a desired architecture. Additionally, we develop micro-benchmarks to demonstrate best-practices in C coding styles that produce high-quality hardware with minimal development effort. Finally, we suggest language, tool, and methodology improvements to improve upon the current state-of-the-art in HLS.

Verification of modern day electronic circuits has become the bottleneck for the timely delivery of complex SoC designs. We develop a novel cross-layer hardware/software co-simulation framework that can effectively debug and verify an SoC design. We combine high-level C/C++ software with cycle-accurate SystemC hardware, uniquely identify various types of bugs, and help the hardware designer localize them. Experimental results show that we are able to detect and aid in localization of logic bugs from both C/C++ specifications as well as the high-level synthesis engine itself. Our framework is fully automated, representing an important step forward targeting fast and effective SoC design verification.

Post-silicon validation and debug challenges of system-on-chips (SoCs) are getting increasingly difficult. As we reach the limits of Dennard scaling, efforts to improve system performance and energy efficiency have resulted in the integration of a wide variety of complex hardware accelerators in SoCs. Hence, it is essential to address post-silicon validation and debug of hardware accelerators. High-level synthesis (HLS) is a promising technique to rapidly create customized hardware accelerators. In this paper, we present the Hybrid Quick Error Detection (H-QED) approach that overcomes post-silicon validation and debug challenges for hardware accelerators by leveraging HLS techniques. H-QED improves error detection latencies (time elapsed from when a bug is activated to when it manifests as an observable failure) by 2 orders of magnitude and bug coverage 3-fold compared to traditional post-silicon validation techniques. H-QED also uncovered previously unknown bugs in the CHStone benchmark suite, which is widely used by the HLS community. H-QED incurs less than 2% chip-level area overhead with negligible performance impact, and we also introduce techniques to minimize any possible intrusiveness introduced by H-QED.

In this study, we propose a low-cost approach to error detection for arithmetic orientated data paths by performing lightweight shadow computations in modulo-3 space for each main computation. By leveraging the binding and scheduling flexibility of high-level synthesis, we detect errors through diverse binding and minimize area cost through intelligent checkpoint scheduling and modulo-3 reducer sharing. We evaluated our technique with 12 high-level synthesis benchmarks using FPGA emulated netlist-level error injection. We observe coverages of 99.13% for stuck-at faults, 99.46% for soft errors, and 99.67% for timing errors with a 25.7% area cost and negligible performance impact. Leveraging error detection latencies on the order of 10 cycles (3 orders of magnitude faster than end result check) for soft errors, we also explore a rollback recovery method with an additional area cost of 28.0%, observing a 175x increase in reliability against soft errors.