Sub-microwatt analog VLSI trainable pattern classifier

The design and implementation of an analog system-on-chip template-based pattern classifier for biometric signature verification at sub-microwatt power is presented. A programmable array of floating-gate subthreshold MOS translinear circuits matches input features with stored templates and combines the scores into category outputs. Subtractive normalization of the outputs by current-mode feedback produces confidence scores which are integrated for category selection. The classifier implements a support vector machine to select programming values from training samples. A two-step calibration procedure during programming alleviates offset and gain errors in the analog array. A 24-class, 14-input, 720-template classifier trained for speaker identification and fabricated on a 3 mm × 3 mm chip in 0.5 μm CMOS delivers real-time recognition accuracy on par with floating-point emulation in software. At 40 classifications per second and 840 nW power, the processor attains a computational efficiency of 1.3 × 10¹² multiply-accumulates per second per Watt of power.