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Spiking Neural Network for Asset Allocation Implemented Using the TrueNorth System

Asset allocation is a compute intensive combinatorial optimization problem commonly tasked to autonomous decision making systems. However, cognitive agents interact in real time with their environment and are generally heavily power constrained. Thus, there is strong need for a real time asset allocation agent running on a low power computing platform to ensure efficiency and portability. As an alternative to traditional techniques, work presented in this paper describes how spiking neuron algorithms can be used to carry out asset allocation. We show that a significant reduction in computation time can be gained if the user is willing to accept a near optimal solution using our spiking neuron approach. As of late, specialized neuromorphic spiking processors have demonstrated a dramatic reduction in power consumption relative to traditional processing techniques for certain applications. Improved efficiencies are primarily due to unique algorithmic processing that produces a reduction in data movement and an increase in parallel computation. In this work, we use the TrueNorth spiking neural network processor to implement our asset allocation algorithm. With an operating power of approximately 50 mW, we show the feasibility of performing portable low-power task allocation on a spiking neuromorphic processor.