David Hsu, Seth Bridges, Miguel Figueroa, Chris Diorio
We present the bump mixture model, a statistical model for analog data where the probabilistic semantics, inference, and learning rules derive from low-level transistor behavior. The bump mixture model relies on translinear circuits to perform probabilistic infer- ence, and floating-gate devices to perform adaptation. This system is low power, asynchronous, and fully parallel, and supports vari- ous on-chip learning algorithms. In addition, the mixture model can perform several tasks such as probability estimation, vector quanti- zation, classification, and clustering. We tested a fabricated system on clustering, quantization, and classification of handwritten digits and show performance comparable to the E-M algorithm on mix- tures of Gaussians.