NeuTigers Inc’s edge-AI algorithms bring AI to resource-constrained devices. This makes AI applications more personalized, scalable and in real time with a higher level of privacy and security than conventional AI. The company is co-founded by Princeton Engineering research.
NeuTigers’ proprietary IP overcomes the traditional challenges of ML that require large datasets,
Expensive compute power and high latency with inference on the cloud.
Its breakthrough approach “shrinks” Neural Network models to be deployed directly on mobile and wearable devices rather than in the cloud. It also reduces the need for large datasets with its innovative approach to high accuracy synthetic data generation.
Making neural networks smaller and efficient (NeST, ChamNet, and TUTOR frameworks):
NeST yields accurate yet very compact DNNs, with a wide range of seed architecture selections.
The ChamNet framework provides an efficient, scalable, and automated Neural Network (NN) architecture adaptation methodology. This adaptation reduces search time. It employs predictive models (accuracy, latency, and energy predictors) to speed up the entire search process by enabling direct performance metric estimation.
Our synthetic data generation toolkit relies on the TUTOR Deep Neural Networks (DNNs) synthesis framework.
TUTOR produces synthetic data in three steps:
NeuTigers synthetic data library
Reduces the need for labeled data by 5.9X. Improves output accuracy by 3.4% despite using a smaller sample size during the learning phase.
Uses fewer samples than Generative Adversarial Networks (GANs).
Refer to the following article for details and performance analysis: TUTOR
Read about our SynthDeep product
Most of the Cyber-Physical Systems (CPS) and Internet-of-Things (IoT) devices are energy-constrained, which makes them unable to implement elaborate cryptographic protocols and primitives and other conventional security measures across the software, hardware, and network stacks.
Another challenge in securing CPS/IoT is the large amount of accessible data generated by the numerous communication channels among devices.
NeuTigers’s ML-based approach systematically generates new exploits in a CPS/IoT framework. We call this approach SHARKS (Smart Hacking Approaches for RisK Scanning).
ML operates at both system and user levels to predict unknown exploits against CPS/IoT.
The GRAVITAS model comes into play to secure CPS/IoT devices.
The core IP presented above makes real-time disease detection and long-term monitoring through wearable sensors more efficient, private and secure.
We have two platforms in this space – CareDeep and StarDeep.
By combining wearable medical sensors (WMSs) and machine learning, StarDeep makes it possible to detect and diagnose diseases in a non-invasive way.
This approach exploits the superior knowledge distillation capability of machine learning to extract medical insights from health-related physiological signals, establishing digital biomarkers for various disease states. To collect physiological signals, we use commercially available devices such as smartwatches, smartphones, and readings from galvanic skin response, oxygen saturation, and blood pressure sensors. After training the models, our model only requires two minutes of data collection for local inference on the device, yielding real-time results that preserve privacy.
StarDeep models can be adapted to different models of smartwatches and smartphones.
Each of the StarDeep products undergoes rigorous clinical trials under the supervision of trained clinicians. All the physiological signals and questionnaire data are collected under Institutional Research Board (IRB) approval.
CovidDeep detects Covid-19 status using device data as explained above and some easy-to-answer questions in a questionnaire whose answers can be obtained through a smartphone application. We have reached a test accuracy of 98.1%.
CovidDeep performs well on false positive rate, false-negative rate, and F1 score.
Refer to the following article for details and performance analysis: CovidDeep
Read the CovidDeep case study
MHDeep diagnoses three important mental health disorders: schizoaffective, major depressive, and bipolar. This has the potential to enhance the ability of the physician to intervene quickly when mental health conditions deteriorate.
Refer to the following article for details and performance analysis: MHDeep
DiabDeep classifies a person’s diabetes status: type 1, type 2 or healthy. The model achieves a 96.3% (95.3%) accuracy in classifying people with diabetes against healthy individuals.
Refer to the following article for details and performance analysis: DiabDeep
Using Machine Learning to Address Fundamental Human Needs
NeuTigers has developed an ML model embedded in smartphones to understand its user. We call it “Your Smartphone Understands You (YSUY).” YSUY uses wearable medical sensors to understand physical, mental, and 4-class (2-class) emotional states with 91.2%, 91.1%, and 96.9% (99.1%) accuracy.
YSUY is a promising candidate for adapting ML models to human-centric needs. We have developed a smartphone app for YSUY and performed experiments with it in real-life situations without limiting the users to specific experimental protocols.
Refer to the following article for details and performance analysis: YSUY