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 deep 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 deep net runs well on low-end hardware, but it can provide 10x the throughput of conventional nets on higher-end MCUs and FPGAs. It will be sent to you as optimized C code, free from floating-point ops or multiplications.
Data must be organized into two CSV files: features; labels. They must not exceed 10 MiB and 3 MiB in size, respectively. They must consist of integer values only and not have column 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