Neuromorphic silicon neurons and large-scale neural networks: challenges and opportunities

• Published in: Frontiers in neuroscience 2011-01, Vol.5, p.108-108
• Main Authors: Poon, Chi-Sang, Zhou, Kuan
• Format: Article
• Language: English
• Subjects: artificial intelligence; Capacitance; CMOS; Complementary metal-oxide-semiconductor transistor; computational neuroscience; Computers; Cost control; Digital computers; Ion channels; large-scale neural networks; Medical equipment; Neural networks; neuromorphic engineering; Neurons; Neuroscience; Semiconductors; Silicon; Silicon neurons; Sin; Software; Transistors; Voltage
• ISSN: 1662-4548; 1662-453X; 1662-4548
• DOI: 10.3389/fnins.2011.00108

According to Liu and Delbrück (2007), digital computers are 104–108 less efficient than biological neurons. To put this in perspective, traditional subthreshold current-mode differential pair circuits commonly used for emulating the sigmoidal current–voltage relationship of ion channels have a limited input voltage dynamic range of < ±100 mV. Since typical CMOS threshold voltage may vary by ±20 mV (3 standard deviations) or more (ITRS, 2007), the worst-case mismatch errors for single devices could be in excess of 100 mV or 100% across the chip and may be further compounded as network size increases and the temperature varies especially for deep submicron processes. The good news is that the trend is rapidly changing in recent years due to phenomenal demands for low-power system-on-chip (SoC) applications such as smartphones, wearable electronics, portable medical devices, etc. [...]a newly available fully depleted silicon-on-insulator technology optimized for ultra-low-power subthreshold circuit applications allows significant reduction in threshold voltage variation and device capacitance when compared with conventional CMOS transistors (Vitale et al., 2011).