EMBEDL TO DEVELOP NEXT-GENERATION EFFICIENT AI PLATFORM IN VEDLIOT Autonomous vehicles and devices for intelligent homes are becoming increasingly complex. These applications involve large collaborative systems which are powered by AI-based algorithms distributed...
We help customers build extraordinary
Deep Learning based products.
Our award winning Deep Learning Optimization Engine optimizes your Deep Learning model for deployment (inference) to meet your requirements of:
- Execution Time (Latency)
- Runtime Memory Usage
- Power Consumption
EmbeDL enables you to deploy Deep Learning on less expensive hardware, using less energy and shorten the product development cycle.
EmbeDL interfaces with the commonly used Deep Learning development frameworks, e.g. Tensorflow and Pytorch. EmbeDL also have world leading support for hardware targets including CPUs, GPUs, FPGAs and ASICs from vendors like Nvidia, ARM, Intel and Xilinx.
We are happy to answer any questions and/or demonstrate EmbeDL on your Deep Learning model(s)!
By using state-of-the-art methods for optimizing Deep Neural Networks, we can achieve a significant decrease in execution time and help you reach your real time requirements.
FOOTPRINT IN DEVICE
The EmbeDL Optimization Engine automatically reduces the number of weights , and thus size of the model, to make it suitable to be deployed to resource constraint environments such as embedded systems
The tools are fully automatic, which reduces the need for time consuming experimentation and thus shorter time-to-market. It also frees up your data scientists to focus on their core problems.
Energy is a scarce resource in embedded systems and our optimizer can achieve an order of magnitude reduction in energy consumption for the Deep Learning model execution.
By optimizing the Deep Learning model, cheaper hardware can be sourced that still meets your system requirements leading to improved product margins.
Optimizing and deploying our customers’ Deep Learning models to embedded systems is what we do. By outsourcing this to us, your team can then focus on your core problems.