While technology scaling has presented many new and exciting opportunities, new design challenges have arisen due to increased density, and delay and power variations. High-level synthesis has been touted as a solution to these problems, as it can significantly reduce the number of man hours required for a design by raising the level of abstraction. In this paper, we propose a new variation-aware high-level synthesis binding/module selection algorithm, named FastYield, which takes into consideration multiplexers, functional units, registers, and interconnects. Additionally, FastYield connects with the lower levels of the design hierarchy through its inclusion of a timing driven floorplanner guided by a statistical static timing analysis (SSTA) engine which is used to modify/enhance the synthesis solution. FastYield is able to incorporate spatial correlations of process variations in its optimization, which are shown to affect performance yield. On average, FastYield achieves a clock period that is 14.5% smaller, and a performance yield gain of 78.9%, when compared to a variation-unaware algorithm. By making use of accurate timing information, FastYield's rebinding improves performance yield by an average of 9.8% over the initial binding, for the same clock period. To the best of our knowledge, this is the first high-level synthesis binding/module selection algorithm that is layout-driven and variation aware.

Glitches (i.e. spurious signal transitions) are major sources of dynamic power consumption in modern FPGAs. In this paper, we present an FPGA-targeted, glitch-aware, high-level binding algorithm for power and area reduction, accomplished via dynamic power estimation and multiplexer balancing. Our binding algorithm employs a glitch-aware dynamic power estimation technique derived from the FPGA technology mapper in [6]. High-level binding results are converted to VHDL, and synthesized with Altera's Quartus II software, targeting the Cyclone II FPGA architecture. Power characteristics are evaluated with the Altera Power-Play Power Analyzer. The binding results of our algorithm are compared to LOPASS, a state-of-the-art low-power high-level synthesis algorithm for FPGAs. Experimental results show that our algorithm, on average, reduces toggle rate by 22% and area by 9%, resulting in a decrease in dynamic power consumption of 19%. To the best of our knowledge this is the first high-level binding algorithm targeting FP-GAs that considers glitch power.

Billions of devices on a chip is around the corner and the trend of deep submicron (DSM) technology scaling will continue for at least another decade. Meanwhile, designers also face severe on-chip parameter variations, soft/hard errors, and high leakage power. How to use these billions of devices to deliver power-efficient, high-performance, and yet error-resilient computation is a challenging task. In this paper, we attempt to demonstrate some of our perspectives to address these critical issues. We elaborate on variation-aware synthesis, holistic error modeling, reliable multicore, and synthesis for application-specific multicore. We also present some of our insights for future reliable computing.