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This article proposes an efficient and accurate embedded motor imagery-based brain–computer interface (MI-BCI) that meets the requirements for wearable and real-time applications. To achieve a suitable accuracy considering hardware constraints, we explore BCI transducer algorithms, among which Infinite impulse response (IIR) filter, common spatial pattern, and support vector machine are used to preprocess, extract features, and classify data, respectively. With our hardware implementation of these tasks, we have achieved an accuracy of 77%. Our system is designed at register transfer level (RTL) targeting an ASIC implementation, which significantly decreases power consumption, latency, and area compared to the state-of-the-art (SoA) architectures for embedded BCI systems. To this end, we fold IIR filters using time-shared and RAM-based techniques and use hardware-friendly algorithms for the implementation of other tasks. The RTL design is realized on 45 nm CMOS technology consuming 4 mW power and 0.25 mm2 area, which outperforms the SoA platforms for embedded BCI systems. To further illustrate the outperformance of our design, the proposed architecture is implemented on Virtex-7 field program gate array as a prototyping platform consuming 6 μJ energy with 1.52% area utilization. [ABSTRACT FROM AUTHOR]

This article proposes a methodology for critical path selection and delay sensor insertion for aging monitoring in field-programmable gate arrays (FPGAs). The aging information can be used to reconfigure FPGAs to achieve better performance or wear leveling. To filter out the paths aging less aggressively, our methodology decides based on both physical-level parameters [e.g., path delay, process variation, temperature, static stress (duty cycle), and dynamic stress (switching activity) as well as aging-relevant parameters (e.g., fan-out, and endpoint physical location)]. After selection of an optimal set of paths, age sensors, introduced in our earlier work, are placed in a distributed fashion throughout the FPGA area to provide aging information of its various parts as they are being used. Accurate aging models for FPGAs are required to determine the contribution of the above-mentioned parameters in aging. In this article, ASICs’ aging models are adapted for FPGAs through measurement-based fitting approach. The experimental results using various benchmarks reveal that our algorithm selects paths with a minimum error. [ABSTRACT FROM AUTHOR]