FlexiNeura

Elevator Pitch: Imagine a world where your everyday objects possess cognitive abilities—FlexiNeura makes that possible by pioneering the integration of intelligent, low-power, flexible electronics into the very fabric of everyday life. From smart wearables that seamlessly adapt to your needs to interactive packaging that understands your touch, FlexiNeura is at the forefront of the next electronics revolution.

Concept

Development of ultra-low power, flexible electronics tailored for embedded machine learning applications utilizing an automated framework that approximates neural network functions for printed electronics.

Objective

To create affordable, flexible, and energy-efficient machine learning hardware that can be embedded into a variety of environments and products.

Solution

Employ an automated framework based on Approximate Computing to design MLP classifiers that can be printed, enabling intricate machine learning computations on ultra-low power, flexible substrates.

Revenue Model

B2B sales of customized MLP classifiers to electronics manufacturers, licensing of technology and design framework, and providing design services for bespoke MLP solutions.

Target Market

Wearable tech, IoT device manufacturers, smart packaging, and healthcare monitoring device companies.

Expansion Plan

Start with partnerships in the wearable sector, then scale to IoT and healthcare, and finally target large-scale electronics manufacturers for integration into a broad range of consumer products.

Potential Challenges

Technical challenges in maintaining performance and accuracy with Approximate Computing, scaling production, and ensuring reliability under various conditions.

Customer Problem

The need for embedded machine learning solutions in products that are flexible, cost-effective, and have ultra-low power consumption.

Regulatory and Ethical Issues

Compliance with electronics and environmental standards, and addressing data privacy concerns for machine learning applications.

Disruptiveness

Disrupts the traditional rigid electronics market by offering a new class of embedded machine learning computation that can be tailored to any form factor.