Current trends in technology scaling foreshadow worsening transistor reliability as well as greater numbers of transistors in each system. The combination of these factors will soon make long-term product reliability extremely difficult in complex modern systems such as systems on a chip (SoC) and chip multiprocessor (CMP) designs, where even a single device failure can cause fatal system errors. Resiliency to device failure will be a necessary condition at future technology nodes. In this work, we present a network-onchip (NoC) routing algorithm to boost the robustness in interconnect networks, by reconfiguring them to avoid faulty components while maintaining connectivity and correct operation. This distributed algorithm can be implemented in hardware with less than 300 gates per network router. Experimental results over a broad range of 2D-mesh and 2D-torus networks demonstrate 99.99% reliability on average when 10% of the interconnect links have failed.
Gate oxide breakdown is a key factor limiting the useful lifetime of an integrated circuit. Unfortunately, the conventional approach for full chip oxide reliability analysis assumes a uniform oxide-thickness for all devices. In practice, however, gate-oxide thickness varies from die-to-die and within-die and as the precision of process control worsens an alternative reliability analysis approach is needed. In this work, we propose a statistical framework for chip level gate oxide reliability analysis while considering both die-to-die and within-die components of thickness variation. The thickness of each device is modeled as a distinct random variable and thus the full chip reliability estimation problem is defined on a huge sample space of several million devices. We observe that the full chip oxide reliability is independent of the relative location of the individual devices. This enables us to transform the problem such that the resulting representation can be expressed in terms of only two distinct random variables. Using this transformation we present a computationally efficient and accurate approach for estimating the full chip reliability while considering spatial correlations of gateoxide thickness. We show that, compared to Monte Carlo simulation, the proposed method incurs an error of only 1∼6% while improving the runtime by around three orders.
Soft errors in logic are emerging as a significant reliability problem for VLSI designs. This paper presents novel circuit optimization techniques to mitigate soft error rates (SER) of combinational logic circuits. First, we propose a gate sizing algorithm that trades off SER reduction and area overhead. This approach first computes bounds on the maximum achievable SER reduction by resizing a gate. This bound is then used to prune the circuit graph, arriving at a smaller set of candidate gates on which we perform incremental sensitivity computations to determine the gates that are the largest contributors to circuit SER. Second, we propose a flipflop selection method that uses slack information at each primary output node to determine the flipflop configuration that produces maximum SER savings. This approach uses an enhanced flipflop library that contains flipflops of varying temporal masking ability. Third, we propose a unified, cooptimization approach combining flipflop selection with the gate sizing algorithm. The joint optimization algorithm produces larger SER reductions while incurring smaller circuit overhead than either technique taken in isolation. Experimental results on a variety of benchmarks show SER reductions of 7.9X with gate sizing, 6.6X with flipflop assignment, and 28.2X for the combined optimization approach, with no delay penalties and area overheads within 5-6%. The runtimes for the optimization algorithms are on the order of 1-3 minutes.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.