High accuracy, low power audio AI
Train and deploy edge devices in the cloud
Train high accuracy, low power audio processing models in an easy-to-use cloud platform. Reduce product development time and cost by taking advantage of the edge device expertise we have built into the training and deployment process.
NengoEdge uses Legendre Memory Units (LMUs), a proprietary recurrent neural network architecture that provides provably optimal representation of time series data. In addition, NengoEdge uses a technique called Hardware Aware Training to fine-tune your model to the edge hardware platform of your choice. This allows NengoEdge to achieve state-of-the-art accuracy on keyword spotting tasks, as well as other time series applications.
NengoEdge models are designed to be deployed in edge applications with constrained power and memory budgets. NengoEdge automates all the details of efficiently mapping your model on to low power edge devices, taking care of things like weight/activity quantization and op kernel selection in order to optimize power and resource usage. However, it is still often the case that power consumption represents a tradeoff, where you have to balance accuracy against your available resource budget. NengoEdge makes it easy to explore different models and different edge devices by removing the device-specific development time, and exposing a wide range of performance metrics. This lets you rapidly prototype different options so that you can find the sweet spot for your applications.
Easy to use
Training and deploying models for edge hardware can be complicated, as each device has its own advantages, limitations, and implementation quirks. This can lead to long development times, and getting locked in to a specific edge device once that development time has been sunk in to a project. NengoEdge takes care of all the complicated details involved in training and deploying models for edge hardware. It provides you with an easy-to-use interface that lets you control the things that matter, while NengoEdge takes care of the device-specific implementation under the hood. This gets you up and running with a minimum of in-house development time.
Visit edge.nengo.ai to sign up for the beta!
Legendre Memory Units
Time series problems include processing of language, audio, biosignals, RF, network traffic and so on -- anything where the order of the data matters. We have invented Legendre Memory Units (LMUs), which are provably optimal at compressing time series, resulting in increased efficiency and accuracy compared to LSTMs and Transformers.
Time Series Processor
Process time series including speech, language, audio, biosignals, RF signals, network traffic, and more at the edge. Our TSP provides extremely low power usage, low latency, and low cost.