Route 2

 AI for autonomous navigation


Overview of Klepsydra AI

In order to process all the amount of data coming from all the different sensors in a car, Klepsydra SDK is the perfect solution to boost data processing with no data loss. However, for autonomous navigation the use of AI algorithms will be critical. For that reason Klepsydra has developed an AI framework called Klepsydra AI.
Klepsydra AI is a high performance deep neural network engine for edge computers. Customers can deploy existing or new trained models on the edge using Klepsydra AI, in the same manner as with standard edge AI solutions. Klepsydra AI offers three main benefits:

  • Process up to 5x more data with AI
  • Reduce power consumption
  • Compatible with main providers of AI software.

Klepsydra AI can deploy on the edge pre-existing or new models that customers might have developed and trained.

Core benefits of AI for autonomous navigation

Safety and reliability
Real-time edge AI. Klepsydra AI can process data in real time with low latency.
Predictable edge AI. Klepsydra AI is substantially more stable, predictable and deterministic than other edge solutions.


Less hardware cost. With Klepsydra AI, more data can be processed on the same hardware, with less power and less memory and without any cloud computing support.


Klepsydra AI is compatible with most AI formats and AI software solutions. It also accept input data in a variety of formats including images and time series.
Furthermore, Klepsydra can be deploy to several edge computers including, but not limited to, Odroid XU4, RaspberryPi4, Intel NUC, etc.

Figure 1. Klepsydra AI setup.

Technical specs


Klepsydra AI is an inference engine for Deep Neural Networks (DNN) aimed at Edge computing applications.
Klepsydra AI has the following modules and APIs:

  • Application API. The inference API that includes instantiation of the model and asynchronous inference API, I.e., callback API.
  • Model importer API.
  • Performance configuration module. This model allows fine performance tuning of the AI model deployment.

Core features

Klepsydra has three main core features:

  • 2x to 6x increase in data processing capabilities with respect to standard techniques (e.g. OpenCV and TensorFlowLite)
  • 30%-50% less power consumption with respect to standard techniques.
  • Accepts ONNX, TensorFlow and Caffe sequential models.

Figure 2. Power consumption benchmark comparison for AlexNet DNN on RaspberryPi4.


Klepsydra is platform independent with the following technical requirements:

  • Operating system present in the target computer
  • Target computer with atomic operation set
  • C++11 complier for the target computer
  • Eigen3 and ONNX software packages.

Klepsydra is supported in a growing number of platforms including:

  • Operating system: Linux.
  • Processors: ARM (V8, Cortex A7, Cortex A9), x86 64 and 32 bits.


Figure 3. Processed data volume benchmark comparison for AlexNet DNN on RaspberryPi4.

Compatibility features

  • Supported languages: C++, C, NodeJS, Python
  • Data format: Float matrices, time series and OpenCV matrix objects (cvMat)
  • Model format: ONNX, TensorFlow and Caffe.

Request our professional trial

We offer a 90 days trial license including email support. Phone and onsite support and training can be requested.

Please fill the form below and our team will be in contact to provide access to download our products.