Neuromorphic analog spiking-modulator for audio signal processing
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 or...
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| Veröffentlicht in: | Analog integrated circuits and signal processing 2021, Vol.106 (1), p.261-276 |
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| container_title | Analog integrated circuits and signal processing |
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| creator | Ferreira, Pietro M. Nebhen, Jamel Klisnick, Geoffroy Benlarbi-Delai, Aziz |
| description | 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
μ
m
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
. |
| doi_str_mv | 10.1007/s10470-020-01729-3 |
| format | Article |
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98.6
μ
m
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
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98.6
μ
m
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
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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
μ
m
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
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| ispartof | Analog integrated circuits and signal processing, 2021, Vol.106 (1), p.261-276 |
| issn | 0925-1030 1573-1979 |
| language | eng |
| recordid | cdi_hal_primary_oai_HAL_hal_02948290v1 |
| source | SpringerNature Journals |
| subjects | Artificial vision Circuits Circuits and Systems CMOS Electrical Engineering Electronics Energy efficiency Engineering Engineering Sciences Micro and nanotechnologies Microelectronics Microprocessors Power management Signal processing Signal,Image and Speech Processing Silicon Spiking Substrates |
| title | Neuromorphic analog spiking-modulator for audio signal processing |
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