We describe analog and mixed-signal primitives for implementing adaptive signal-processing algorithms in VLSI based on anti-Hebbian learning. Both on-chip calibration techniques and the adaptive nature of the algorithms allow us to compensate for the effects of device mismatch. We use our primitives to implement a linear filter trained with the least-mean squares (LMS) algorithm and an adaptive decorrelation network that improves the convergence of LMS. When applied to an adaptive code-division multiple-access (CDMA) despreading application, our system, without the need for power control, achieves more than 100/spl times/ improvement in the bit-error ratio in the presence of high interference between users. Our 64-tap linear filter uses 0.25mm/sup 2/ of die area and dissipates 200/spl mu/W in a 0.35/spl mu/m CMOS process.
['Miguel Figueroa', 'Esteban Matamala', 'Gonzalo Carvajal', 'Seth Bridges']
Adaptive signal processing in mixed-signal VLSI with anti-Hebbian learning