Abstract
We propose a fully integrated common-source amplifier based analog artificial neural network (ANN). The performance of the proposed ANN with a custom non-linear activation function is demonstrated on the breast cancer classification task. A hardware-software co-design methodology is adopted to ensure good matching between the software AI model and hardware prototype. A 65 nm prototype of the proposed ANN is fabricated and characterized. The prototype ANN achieves 97% classification accuracy when operating from a 1.1 V supply with an energy consumption of 160 fJ/classification. The prototype consumes 50 μ W power and occupies 0.003 mm 2 die area.
Highlights
Conventional artificial intelligence (AI) and machine learning systems are implemented on remote “cloud” servers comprising graphical processing units (GPUs)
We propose a fully-integrated artificial neural network (ANN) implemented entirely using analog circuits comprising of custom non-linear activation function and current-domain multiply-and-accumulate (MAC)
The hidden layer of the ANN applies non-linear transformation of input from one vector space to another and such hierarchical non-linear transformation provides a unique way to increase the separation between the decision boundaries and improve the efficiency of the machine learner over any standard linear machine algorithm
Summary
Conventional artificial intelligence (AI) and machine learning systems are implemented on remote “cloud” servers comprising graphical processing units (GPUs). With the rapid growth of edge devices, there is a need for condensing and implementing AI algorithms in energy-constrained edge devices. While AI algorithms are conventionally realized in hardware using digital circuits, analog implementation of AI algorithms can potentially reduce energy consumption by several orders of magnitude [1] by eliminating data movement from the central processing unit (CPU) to the memory. We propose a fully-integrated artificial neural network (ANN) implemented entirely using analog circuits comprising of custom non-linear activation function and current-domain multiply-and-accumulate (MAC). The proposed CMOS implementation of the ANN can be considered as a multi-layer fully connected neural nets that consist of an input layer, a non-linear hidden layer and an output layer. The hidden layer of the ANN applies non-linear transformation of input from one vector space to another and such hierarchical non-linear transformation provides a unique way to increase the separation between the decision boundaries and improve the efficiency of the machine learner over any standard linear machine algorithm (e.g., logistic regression [7], SVM [8])
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