A Novel Neuromorphic Processors Realization of Spiking Deep Reinforcement Learning for Portfolio Management
Seyyed Amirhossein Saeidia, Forouzan Fallahb, Soroush Barmakic and Hamed Farbehd
Department of Computer Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran
asahsaeedi@aut.ac.ir
bforuzan.fallah@aut.ac.ir
csr.barmaki@aut.ac.ir
dfarbeh@aut.ac.ir
ABSTRACT
The process of constantly reallocating budgets into financial assets, aiming to increase the anticipated return of assets and minimizing the risk, is known as portfolio management. Processing speed and energy consumption of portfolio management have become crucial as the complexity of their real-world applications increasingly involves high-dimensional observation and action spaces and environment uncertainty, which their limited onboard resources cannot offset. Emerging neuromorphic chips inspired by the human brain increase processing speed by up to 500 times and reduce power consumption by several orders of magnitude. This paper proposes a spiking deep reinforcement learning (SDRL) algorithm that can predict financial markets based on unpredictable environments and achieve the defined portfolio management goal of profitability and risk reduction. This algorithm is optimized for Intel’s Loihi neuromorphic processor and provides 186x and 516x energy consumption reduction compared to a high-end processor and GPU, respectively. In addition, a 1.3x and 2.0x speed-up is observed over the high-end processors and GPUs, respectively. The evaluations are performed on cryptocurrency market benchmark between 2016 and 2021.
Keywords: Neuromorphic Computing, Deep Reinforcement Learning, Portfolio Management, Loihi.
