Technology Validation: Sparsity Enables 50x Performance Acceleration in Deep Learning Networks

Numenta Engineering • Whitepaper

Today’s machine learning techniques have accomplished a great deal but are widely acknowledged to be running into fundamental limitations, including their need for enormous compute power. Unlike deep learning networks used in machine learning, the brain is highly efficient, consuming less power than a lightbulb. How is the brain so efficient? There are many reasons, but at its foundation is the concept of sparsity. At any given time, no matter where you look in the brain, only a small percentage of neurons in the brain are active, as the brain stores and processes information as sparse representations. In addition, unlike deep learning networks, the connectivity between neurons in the brain is also highly sparse.


This paper demonstrates the application of Numenta’s brain-inspired, sparse algorithms to machine learning. We used these algorithms on Xilinx™ FPGAs (Field Programmable Gate Array) and the Google Speech Commands (GSC) dataset to show the benefits of leveraging sparsity in order to scale deep learning models. Our results show that sparse networks are 50 times faster than non-sparse networks on an inference task with competitive accuracy.

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Standard dense layer vs. our sparse layer. Our sparse layer contains both sparse weights and sparse activations. 

Frequently asked questions

Q. What was the purpose of this paper?

This paper demonstrates how sparsity can achieve significant acceleration and power efficiencies while maintaining competitive accuracy in deep learning networks. It also aims to break through the performance bottleneck in today’s machine learning techniques in order to achieve scaling improvements that enable significant energy savings and reduce power consumption.

Q. What is the key takeaway?

The best sparse network configuration can process data 50 times faster than the dense implementation on the platform. The overall system speedup is due in part to the speed of a single sparse network combined with the fact that we can fit more sparse networks on each chip. On a per network basis, sparse networks are significantly faster than dense networks on all platforms.

Q. How were the performance tests conducted?

We chose the Google Speech Commands (GSC) dataset, which consists of 65,000 one-second long utterances of keywords spoken by thousands of individuals. The task is to recognize the word being spoken from the audio signal. We used an FPGA (Field Programmable Gate Array) as the hardware platform to run the performance tests because of the flexibility it provides in handling sparse data efficiency. We do expect that current generation CPUs and GPUs can achieve benefits from sparsity as well, though not as dramatic as in FPGAs. In the future, we propose exciting architecture enhancements to CPUs and GPUs that would enable greater use of sparsity for substantial performance gains.

The dense and sparse networks are run on the same card, sequentially, by downloading the selected network into the card and then feeding in input data. We compare performance using three different metrics: throughput, power usage, and resource utilization.

Q. What are the implications?

This dramatic speed improvement could provide great benefits, enabling:

  • Implementation of far larger networks using the same resource
  • Implementation of more copies of networks on the same resource
  • Implementation of sparse networks on edge platforms with limited resources where dense networks do not fit
  • Massive energy savings and lower costs due to scaling efficiencies

This technology demonstration is the beginning of a much broader roadmap that we have laid out, based on our deep neuroscience research. Beyond sparsity, as we add more elements of our neocortical model, we expect additional benefits in unsupervised learning, robustness and sensorimotor behavior.

Q. Where can I find out more?

We have several additional resources for people who want to learn more:

Numenta Engineering • Whitepaper

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