Electronic systems increasingly suffer from component variation, thermal hotspots, uneven wearout, and other subtle physical phenomena. Systems based on FPGAs have unique opportunities for adapting to such effects. Required, however, is a low-cost, fine-grained method for sensing physical parameters. This paper presents an approach to on-line sensing that includes a compact multi-use sensor implemented in reconfigurable logic, methods for instrumenting an application, and enhanced measurement procedures. The sensor utilizes a highly-efficient counter and improved ring oscillator, and requires just 8 LUTs. We describe how to measure variations in delay, static power, dynamic power, and temperature. We demonstrate the proposed approach with an experimental system based on a Virtex-5. The system is instrumented with over 100 sensors with a total overhead of only 1.3%. Results from thermally-controlled experiments provide some surprising insights and illustrate the power of the approach. On-line sensing can help open the door to physically-adaptive computing, including fine-grained power, reliability, and health management schemes for FPGA-based systems.

Abstract—Random physical variations and noise are growing challenges for advanced electronic systems. Field programmable systems can, in principle, adapt to these phenomena, but two main problems must be addressed: how to efficiently characterize random variations and how to perform subsequent optimization. This paper addresses both of these questions. First, an approach to self-test is presented that uses on-chip noise emulation to quickly characterize some of the hidden variations in latches. Our noise-injection experiments demonstrate that there can be significant spreads in latch reliability even with current 65nm field-programmable gate arrays (FPGAs). We detected coefficients of variation as high as 77%. Second, we propose an approach to self-optimization using local resource swapping. Experiments on two FPGAs show improvements in mean-time-between-failures (MTBF) of up to 60%. Keywords-self-adaptation; self-test; self-optimization; variations; transient faults; FPGAs; reconfigurable computing I.

This paper describes the realization of an interconnect Delay Insensitive (DI) FPGA architecture with distributed asynchronous control. This architecture maintains the basic block structure of traditional FPGAs allowing the potential use of existing FPGA design tools in block design. This asynchronous FPGA architecture is mainly aimed at tolerating the unpredictable delay variations caused by process and environment variations in current and future VLSI technology nodes and also targets low power operations, including modes such as dynamic voltage scaling and variable Vdd, as in applications featuring energy harvesting. This is achieved by making the longer inter-block interconnects DI, keeping the computational logic single-rail, and removing global clocks.

Abstract—This paper introduces a set of novel techniques for rapid post-silicon characterization of FPGA timing variability. The existing built-in self-test (BIST) methods work by incrementing the clock frequency until timing failures occur within the combinational circuit-under-test (CUT). A standing challenge for industrial adoption of post-silicon device profiling by this method is the time required for the characterization process. To perform rapid and accurate delay characterization, we introduce a number of techniques to rapidly scan the CUTs while changing the clock frequency using off-chip and on-chip clock synthesis modules. We next find a compact parametric representation of the CUT timing failure probability. Using this representation, the minimum number of frequency samples is determined to accurately estimate the delay for each CUT within the 2D FPGA array. After that, we exploit the spatial correlation of the delays across the FPGA die to measure a small subset of CUT delays from an array of CUTs and recover the remaining entries with high accuracy. Our implementation and evaluations on Xilinx Virtex 5 FPGA demonstrate that the combination of the new techniques reduces the characterization timing overhead by at least three orders of magnitude while simultaneously reducing the required storage requirements. I.