Accelerate AI on Edge

ENAP Use Cases

Build edge AI applications in few hours, not in months!

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AI on Cloud

Challenges of AI on cloud, that necessitates disruptive innovation for AI on Edge solutions

Increasing Costs
With exponential increase in data, and workloads demanded by AI deployed on cloud, it increases the demand for more infrastructure, increasing costs.
Privacy Issues
Cloud poses risks to privacy, for companies processing healthcare, facial recognition and private information of enterprises and individuals.
Connectivity Issues
The demand for more bandwidth is increasing for AI applications that are highly dependent on cloud, leading to serious connectivity issues.
Latency Issues
Industries requiring real time data processing like autonomous cars, surveillance of IOT applications, cannot afford latency.
Power Hungry
Deep learning models get better with more data, thus becoming more power hungry & heavier than previous models, as it evolves in AI capabilities.
Intelligent Devices
As IOT continues to be integral to digital transformation initiatives across industries, it has led to tremendous increase in intelligent devices.
Benefits of AI on Edge

Distributed Intelligence

Bringing all critical AI Workloads near to source of data

Edge AI eliminates all of the drawbacks of AI on cloud, and brings in high performance computing capabilities closer to the source of data, that enables AI-powered analytics, deep learning and other critical applications on edge.

Privacy is maintained by processing the data at the source itself, ownership stays intact when data is processed locally. Edge AI delivers low centralised infrastructure overhead improving cost advantages. Most critically, edge AI delivers zero latency for critical edge applications.

Increase level of automation
Build digital twins for advanced analytics
Real-time decision making
Facilitate Edge inference and training
Edge is the solution

EDGENeural.ai accelerates edge AI adoption

EdgeNeural.ai provides a simple and comprehensive platform to leverage AI capabilities for edge. The platform simplifies the process of deploying AI on edge and helps to build edge AI applications in few hours and not in months/ days.
Overcome lack of standardisation
Lack of standardisation across various hardware available for deploying AI applications, makes it difficult for companies to deploy AI on edge; as every company has its own way to build, optimize, and deploy AI applications.
Integrate seamlessly with existing frameworks
There are various frameworks and tools are available which are open source, but integration to them is difficult and is not straight forward. EDGENeural.ai makes it seamless to integrate across frameworks and tools for edge application
Rearchitect and optimize models for edge easily
As majority of AI today is cloud native, it is essential to rearchitect these models so that it can perform efficiently, consume less memory, and is power optimized to work efficiently on edge. Optimize, and compress AI models to run optimally on small edge hardware
Accelerate time to market
To build an edge AI prototype and edge AI applications, you do not have to invest 6 to 8 months anymore, in deciding what hardware to choose, what framework and tools to select. Nor, do you have to spend thousands of dollars in hiring edge AI developers. Train, optimize and deploy in hours not even in days.

EDGENeural.ai EDGE-AI Platform (ENAP)

Train, optimize, quantise and deploy edge AI neural networks