Deep-Tech Startup Applied Brain Research Inc. Extends Battery Life with Ultra-Low-Power AI Algorithm

Dec 09, 2019, Applied Brain Research

  • Canadian Startup, Applied Brain Research, announces breakthrough at Vancouver NeurIPS Spotlight Talk on 10 Dec 19.
  • Algorithm enables advances in ultra-low-power AI speech, vision and signal processing.
  • Implemented on spiking neuromorphic hardware including Intel’s Loihi Chip.

Toronto, Ontario, Canada – December 10, 2019 – Applied Brain Research (ABR) announce a new algorithm that enables advances in ultra-low-power AI speech, vision and signal processing systems for always-on and edge-AI applications, extending battery life while making them more accurate.

ABR’s announcement demonstrates the potential to realize ultra-low-power instantiations of a large class of algorithms that learn patterns in data, spanning extraordinarily long intervals of time.

Existing LSTM technology fails to scale & limits commercial application

Current algorithms, like Long Short-Term Memories (LSTMs), can learn and predict sequences of data for long periods of time and make it possible for neural networks to learn to process data like speech, video and control signals.

Present in most smart speakers and voice recognition systems, LSTMs are said to be the most financially valuable AI algorithm ever invented (Bloomberg).

LSTMs fail when tasked with learning temporal dependencies in signals that span 1,000 time-steps or more, making them very difficult to scale and limiting commercial application.

About ABR’s breakthrough algorithm

This new algorithm – the Legendre Memory Unit (LMU) – is a neuromorphic algorithm for continuous-time memory that can learn temporal dependencies over millions of time-steps or more. The algorithm is a new INN architecture that enables networks of artificial neurons to classify and predict temporal patterns far more efficiently than LSTMs.

  • The LMU is mathematically derived to implement the continuous-time dynamical system that optimally maintains a scale-invariant representation of time.
  • The ABR LMU obtains the best-known results on permuted sequential MNIST, a difficult RNN benchmark, and has been shown to scale to input sequences spanning hundreds of millions of time-steps.
  • The resulting patterns in spiking activity have also been linked to neural “time cells” observed in the striatum and medial prefrontal cortex in mammalian brains.

Unlike the LSTM, the LMU can be implemented using spiking neurons, thus demonstrating an algorithmic advance that is anticipated to provide leaps in efficiency for solutions to dynamical time-series problems using low-power neuromorphic devices.

Notes to editors

Voelker et al. (2019) found that ABR’s LMU required fewer resources and less computations, whilst providing superior memory and demonstrating state-of-the-art performance of 97.15% on a challenging RNN benchmark compared to 89.86% using LSTMs. Video

The core building block of the LMU has been implemented on spiking neuromorphic hardware including Braindrop (Neckar et al., 2019) and Loihi (Voelker, 2019).

The LMU outperforms both spiking and non-spiking reservoir computers (i.e., liquid state machines and echo state machines) in efficiency and memory capacity when tasked with representing temporal windows of information (Voelker, 2019).

About Applied Brain Research Inc. (ABR)

  • ABR is the maker of the Nengo neuromorphic software development suite which includes the world’s leading multi-platform, visual neuromorphic software compiler, runtime and spiking deep learning platform.

  • Using ABR’s neuromorphic tools, the team at ABR has built Spaun, the world’s largest functional brain model and builds real-time, full-loop AI “brains” for customers in the military, self-driving car, IoT and smartphone markets. (

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Oliver Morgan
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