Implementation
Evolving tailored autonomous capabilities for your systems

Applying Evolutionary AI

SAME HARDWARE, DIFFERENT MATH

Robotics
Autonomous Vehicle

0x

LESS ENERGY with E-AI

0x

FEWER RESOURCES with E-AI

Process

STEP 1

Requirement Gathering
  • Vivum collaborates with the client to gather and analyze technical and performance requirements.

  • Focus on the client’s specific needs and objectives.

In the case of real-time lane assistance, the focus would be on error-corrected performance for ADAS-enabled self-driving cars on unfamiliar or untrained roads.

STEP 2

Training & Modeling
  • Vivum conducts training and modeling of a dynamic neural network on our proprietary cloud platform.

  • Our Evolutionary Training process results in an optimized model tailored to the client’s specific needs.

For real-time lane assistance, the evolutionary training process would focus on developing a model that accurately and efficiently detects and responds to lane markings and road conditions.

STEP 3

Compilation
  • Vivum compiles the evolved model and packages it for seamless integration (over the air), along with necessary documentation and support.

The compiled real-time lane assistance model would be delivered to the client, ready for integration into their ADAS system.

STEP 4

Inferencing

The client can immediately implement the model in three ways. Our dynamic models are compatible with every existing CPU, Microcontroller, or FPGA on any device:

  • CPU-based Systems

  • Controllers/Microcontrollers

  • FPGAs

  • Your Custom ASICs – Our dynamic models are also compatible with custom ASICs existing within your system.

The client can deploy the real-time lane assistance model on their preferred hardware platform.

STEP 5

Testing & Refinement
  • Client conducts extensive testing in simulations and real-world scenarios.

  • Performance metrics and inferencing results are shared with Vivum for analysis. We use this feedback to further refine the training and modeling process.

Real-time lane assistance system would undergo thorough testing in simulations and real-world driving conditions. Feedback from these tests would be used to refine the model further.

STEP 6

Iterative Improvement
  • Vivum provides iterative refinements based on client feedback and changing requirements.

  • Updated models are delivered to the client (over the air), ensuring continuous improvement and optimization.

As the client’s needs evolve and new data becomes available, Vivum will continuously update and optimize the real-time lane assistance model to maintain peak performance and reliability.

FAQ

Ask Us Anything
Is specific hardware required to use the Vivum system, or can it integrate with existing infrastructure?
What is the typical timeframe from requirements gathering to model compilation and implementation stages?
How are dynamic neural models different from conventional AI models?
Why don't many companies offer custom modeling and training of dynamic neural models?
What’s the catch with Vivum’s IP core services?
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