Classifiers for Sensory Data (e.g. tanh( ) ) Key Signal to Symbol - PowerPoint PPT Presentation

Apr 08, 2023 •199 likes •289 views

Classifiers for Sensory Data (e.g. tanh( ) ) Key Signal to Symbol Refinement Two layers: (VMM + nonlinear functions) (e.g. tanh( ) ) Required for Universal Approximator = arbitrary function approximation with infinite (countable) neurons

Classifiers for Sensory Data (e.g. tanh( ) ) Key Signal to Symbol Refinement Two layers: (VMM + nonlinear functions) (e.g. tanh( ) ) Required for Universal Approximator = arbitrary function approximation with infinite (countable) neurons VMM = Vector Matrix Multiplication y = W x XOR: Classic VMM often implemented in FPAA function showing two layers Nonlinear function (e.g. tanh) allows required to for decisions and nonlinear function fully implement approximation
FG VMM VMM in FPAA Routing Fabric (Computing in memory) [Chawla, et. al, CICC 2004] Analog (VMM): ~10-20MMAC/ µ W ~50-100 fJ / MAC Digital ~ 10MMAC/mW @ yield
VMM + k-WTA: Experimental Measurements of Measured XOR Scatter Plot a Universal Approximator 1 single VMM + n-WTA layer = universal approximator [Ramakrishnan, et. al, 2013] [Maass, et. al, 2000] Also 3-Parity, 4-Parity WTA: biologically inspired, good framework for further bio-inspired computing
Classifiers Equivalences with VMM+WTA Invariance: • Constant Weight Offsets • Constant Weight Scaling • Constant Input Offsets � Only requires 1 quadrant mult Equivalence: • VQ • SOM • GMM
Measured XOR Classifier: Different CABs
SoC FPAA: Computing
Experiment Session Perform the 2-input VMM + WTA block for the XOR problem When you start your classifier, the weights might not be quite right. You should change these weights until you get the resulting function and take note of these differences

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