Communications of the ACM
Achieving Green AI with Energy-Efficient Deep Learning Using Neuromorphic Computing
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Deep learning (DL) systems have been widely adopted in many industrial and business applications, dramatically improving human productivity, and enabling new industries. However, deep learning has a carbon emission problem.a For example, training a single DL model can consume as much as 656,347 kilowatt-hours of energy and generate up to 626,155 pounds of CO2 emissions, approximately equal to the total lifetime carbon footprint of five cars. Therefore, in pursuit of sustainability, the computational and carbon costs of DL have to be reduced.
Modeled after systems in the human brain and nervous system, neuromorphic computing has the potential to be the implementation of choice for low-power DL systems. Neuromorphic computing features both neuromorphic algorithms, called spiking neural networks (SNNs), and neuromorphic hardware which are dedicated ASICs optimized for SNNs. Spiking neural networks are regarded as the third generation of artificial neural networks (ANNs), in which spikes (represented by "0" and "1" in the computing system, where "0" means the absence of a spike) are used to transmit information between neurons. With such a spiking mechanism, costly multiplications could be replaced by more energy-efficient additions, mitigating the intensity of the computation. Neuromorphic hardware, on the other hand, has a non-von Neumann "processing in memory" architecture, where computations are integrated into or near a distributed memory architecture. Combined with promising emerging memory devices such as non-volatile resistive and magneto-resistive memories (that is, RRAM and MRAM) to store synaptic weights, both static power and power consumed by data movement are significantly reduced.
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