Train deep nets that can work on low-end hardware, use only a few kB of memory while drawing power in the sub-µW range.
A download link to the trained net will be emailed to you about a day after you upload the data.
This portal lives on the Google Cloud and requires a Google or G Suite account for sign-in. If you use Gmail, you already have a Google account.
After you have signed in, click on Continue.
The Infxl net is ideal for energy-constrained IoT edge devices and wearables. It is targeted at data from temperature, pressure, vibration, acceleration, speed, angular velocity, tilt, flow, smell, taste, and environmental sensors.
The Infxl net runs well on low-end hardware, but it can provide 10x throughput of conventional nets on a higher-end MCU, DSP, or FPGA. It will be sent to you as optimized C code, free from floating-point ops and multiplications.
Data must be organized in the form of 4 CSV files: training-features (max size 16 MB), training-labels (6 MB), validation-features (7 MB), and validation-labels (3 MB). They must consist of integers only and not have headings or missing values.
Each label must have its own column, e.g. two columns for a two-class problem.
Binary columns must be mapped to the set {-127, 127}. Continuous-valued columns must be mapped to the range [-127, 127]. For example, if continuous values are in the range [0, 1], they are to be mapped using int(round(254 * value)) - 127