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This paper presents a storage-efficient learning model titled RecursiveBinary Neural Networks for sensing devices having a limited amount of on-chipdata storage such as < 100's kilo-Bytes. The main idea of the proposed model isto recursively recycle data storage of synaptic weights (parameters) duringtraining. This enables a device with a given storage constraint to train andinstantiate a neural network classifier with a larger number of weights on achip and with a less number of off-chip storage accesses. This enables higherclassification accuracy, shorter training time, less energy dissipation, andless on-chip storage requirement. We verified the training model with deepneural network classifiers and the permutation-invariant MNIST benchmark. Ourmodel uses only 2.28 bits/weight while for the same data storage constraintachieving ~1% lower classification error as compared to the conventionalbinary-weight learning model which yet has to use 8 to 16 bit storage perweight. To achieve the similar classification error, the conventional binarymodel requires ~4x more data storage for weights than the proposed model.

Recursive Binary Neural Network Learning Model with 2.28b/Weight Storage Requirement