Hardware Settings

The Hardware Settings tab allows you to specify the target hardware that your model will be deployed on. You have the option to select a predefined hardware or to define custom hardware resources. ONE AI will create a model that is optimized for your hardware and will run within the constraints that were specified in the Model Settings.

The important settings are just the Hardware Type, the Compute Capability how fast your hardware can do calculations and the Memory Limit how much RAM is available for the calculations. Quantized Calculations are important as well when you want to export to a microcontroller, TPU or FPGA.

Used Hardware

You have the option to select select a preconfigured hardware. If you want to use a different hardware, you can select Custom and enter the hardware specifications in the advanced settings.

Advanced Settings

• Hardware Type: Select the hardware type:
• Prioritize Speed Optimization: Enable this if your hardware, such as a microcontroller, has limited computational capabilities and benefits from a prioritization of speed over memory usage.
• Compute Capability: Specify the computational power of your hardware.
• Compute Capability Unit: The unit you used to specify the Compute Capability.
• 8 Bit Multipliers (DSP Blocks): Define the amount of 8 bit multipliers of your FPGA.
• 8 Bit Multipliers with Sum per DSP Block: The amount of multipliers with sum that is available on your FPGA.
• Prioritize Memory Optimization: Enable this if your hardware, such as an FPGA with limited internal RAM, requires efficient memory usage for higher accuracy with fewer model parameters.
• Memory Limit: Define the amount of available memory. The type of memory depends on your hardware. For example, a GPU would use its VRAM while a CPU would use the system RAM. You can look at the tooltip for more information.
• Memory Limit Unit: The unit you used to specify the Memory Limit.
• Optimize for Parallel Execution: Select this option if you plan to implement the AI as a parallel architecture on FPGAs or ASICs.
• Quantized Calculations: Enable quantization to boost performance. This can slightly reduce accuracy but significantly increases speed. For most applications, especially on microcontrollers, TPUs, FPGAs, or ASICs, quantization is highly recommended.
• Bits per Value: Set the precision level for neural network calculations.