Working with vivum
Implementation
Deploy Dynamic Neural Models in Less Than Two Weeks with Our Proprietary High-Throughput Method, Eliminating the Need for Costly supercluster CPUs and GPUs
Applying Evolutionary AI
same hardware, different math
with E-AI
with E-AI
with E-AI
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?
No additional hardware is needed to benefit from the ViVum system. Our IP core seamlessly integrates with your existing infrastructure, whether you have a standard CPU, controller, FPGA, or even a custom ASIC. The ViVum system is designed to be highly adaptable, allowing you to leverage your current hardware to its full potential while taking advantage of our efficient and dynamic neural models.
What is the typical timeframe from requirements gathering to model compilation and implementation stages?
Without ViVum:
- Duration: 4.5-10 months
- Dynamic neural modeling is costly and time-consuming when using traditional hardware like GPUs or CPU superclusters to ‘train’ a model.[1] [2]
With ViVum:
- Duration: 3-9 days*
- ViVum has developed a proprietary high-throughput method to ‘evolve’, model, train, and deploy bespoke dynamic networks for robots, autonomous cars, high-frequency trading protocols, and more. Our approach doesn’t rely on traditional CPUs or GPUs, enabling significantly faster development times.
*Please note that the duration for steps 4-6 depends on the client’s speed.
How are dynamic neural models different from conventional AI models?
While dynamic neural models and traditional deep learning models both learn from data to make predictions or decisions, there are key differences that make ViVum’s models more efficient, easily deployable, and explainable:
- Efficiency: Our dynamic neural models process information faster and require fewer computational resources (our foundational models are SUBSTANTIALLY “smaller,” more compact), making them ideal for real-time resource-constrained applications.
- Ease of Deployment: ViVum’s models are compatible with various platforms, including CPUs, microcontrollers, FPGAs, and custom ASICs, allowing for seamless integration into existing systems.
- Adaptability: Through our proprietary ‘evolutionary training’ process, ViVum’s dynamic neural models continuously adapt and improve, ensuring optimal performance and accuracy over time.
- Explainable AI: ViVum’s dynamic neural models offer greater transparency and interpretability compared to conventional deep learning models. By employing techniques such as rule extraction, decision trees, and attention-gated routing, our models provide human-readable explanations for their predictions, making them easier to understand, debug, and trust.
Why don't many companies offer custom modeling and training of dynamic neural models?
Bespoke modeling and training of dynamic neural models is complex and resource-intensive, requiring significant computational power and expertise (super computers-CPUs or BPTT-GPUs). This makes the process costly and time-consuming for most companies.
However, ViVum has developed a proprietary high-throughput system for modeling and training these networks efficiently. Our system is the result of nearly a decade of research and development, allowing us to create and train dynamic neural models cost-effectively. This proprietary technology sets ViVum apart in the field of dynamic neural modeling, making this advanced technology more accessible to our clients.
What’s the catch with ViVum’s IP core services?
Transparency is important to us. While we’re developing tools to enable your team to handle the training and modeling process independently, our team will provide these services in the meantime. This ensures you can benefit from dynamic neural models without delay.