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 lm 2 and the substrate leakage to a 1:95 fJ=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 bits spiking-modulator resolution, a maximum of 8 fJ=conv efficiency, and a root-mean-square error of 0:63 mV RMS .

While CMOS technology is currently reaching its limits in power consumption and circuit density, a challenger is emerging from the analogy between biology and silicon. Hardware-based neural networks may drive a new generation of bio-inspired computers by the urge of a hardware solution for real-time applications. This paper redesigns a previous proposed electronic neuron (e-Neuron) in a higher firing rate to reduce the silicon area and highlight a better energy efficiency trade-off. Besides, an innovative schematic is proposed to state an e-Neuron library based on Izhikevichs model of neural firing patterns. Both e-Neuron circuits are designed using 55 nm technology node. Physical design of transistors in weak inversion are discussed to a minimal leakage. Neural firing pattern behaviors are validated by post-layout simulations, demonstrating the spike frequency adaptation and the rebound spikes due to postinhibitory effect in LTS e-Neuron. Presented results suggest that the time to rebound spikes is dependent of the excitation current amplitude. Both e-Neurons have presented a fF/spike energy efficiency and a smaller silicon area in comparison to Izhikevichs library propositions in the literature. CCS CONCEPTS• Hardware → Analog and mixed-signal circuit synthesis; Standard cell libraries; Emerging architectures.