A Low-cost Artificial Neural Network Model for Raspberry Pi
In this paper, a ternary neural network with complementary binary arrays is proposed for representing the signed synaptic weights. The proposed ternary neural network is deployed on a low-cost Raspberry Pi board embedded system for the application of speech and image recognition. In conventional neural networks, the signed synaptic weights of –1, 0, and 1 are represented by 8-bit integers. To reduce the amount of required memory for signed synaptic weights, the signed values were represented by a complementary binary array. For the binary inputs, the multiplication of two binary numbers is replaced by the bit-wise AND operation to speed up the performance of the neural network. Regarding image recognition, the MINST dataset was used for training and testing of the proposed neural network. The recognition rate was as high as 94%. The proposed ternary neural network was applied to real-time object recognition. The recognition rate for recognizing 10 simple objects captured from the camera was 89%. The proposed ternary neural network with the complementary binary array for representing the signed synaptic weights can reduce the required memory for storing the model’s parameters and internal parameters by 75%. The proposed ternary neural network is 4.2, 2.7, and 2.4 times faster than the conventional ternary neural network for MNIST image recognition, speech commands recognition, and real-time object recognition respectively.
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