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Neuromorphic analog spiking-modulator for audio signal processing
Analog Integrated Circuits and Signal Processing ( IF 1.2 ) Pub Date : 2020-10-26 , DOI: 10.1007/s10470-020-01729-3
Pietro M. Ferreira , Jamel Nebhen , Geoffroy Klisnick , Aziz Benlarbi-Delai

While CMOS scaling is currently reaching its limits in power dissipation and circuit density, the analogy between biology and silicon is emerging as a solution to ultra-low-power signal processing. Urgent applications involving artificial vision and audition, including intelligent sensing, appeal original energy efficient and ultra-miniaturized silicon-based solutions. While state-of-the-art is focusing on digital-oriented solutions, this paper proposes a neuromorphic analog signal processor using Izhikevich-based artificial neurons in an analog spiking modulator. A varicap-based artificial neuron is explored reducing the silicon area to \(98.6\;\upmu{\text{m}}^{2}\) and the substrate leakage to a \(1.95 \;{\text{fJ}}/{\text{spike}}\) efficiency. Post-layout simulation results are presented to investigate the high-resolution, high-speed, and full-scale dynamic range for audio signal processing applications. The proposal demonstrates a \(9 \;{\text{bits}}\) spiking-modulator resolution, a maximum of \(8\;{\text{fJ}}/{\text{conv}}\) efficiency, and a root–mean–square error of \(0.63\;{\text{mV}}_{RMS}\) .



中文翻译:

用于音频信号处理的神经形态模拟尖峰调制器

尽管CMOS缩放目前在功耗和电路密度方面已达到其极限,但生物学和硅之间的类比正在成为超低功耗信号处理的解决方案。涉及人工视觉和听觉的紧急应用,包括智能传感,吸引了原始的节能和超小型硅基解决方案。当最新技术专注于面向数字的解决方案时,本文提出了一种使用基于Izhikevich的人工神经元在模拟峰值调制器中的神经形态模拟信号处理器。探索了一种基于varicap的人工神经元,可将硅面积减少至\(98.6 \; \ upmu {\ text {m}} ^ {2} \)并将基底泄漏减少至\(1.95 \; {\ text {fJ} } / {\ text {spike}} \)效率。提出了布局后仿真结果,以研究音频信号处理应用的高分辨率,高速和满量程动态范围。该提案展示了\(9 \; {\ text {bits}} \)尖峰调制器分辨率,最高效率为\(8 \; {\ text {fJ}} / {\ text {conv}} \},且均方根误差为\(0.63 \; {\ text {mV}} _ {RMS} \)

更新日期:2020-10-30
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