Deploying NeuroMem-powered intelligenceTrainable, Responsible, Explainable
Deploying a NeuroMem engine
Collect data, visualize, curate, annotate with labels
Train the neurons with features extracted from annotations
Verify the classification of new datasets, identify uncertainties
Export the knowledge built by the neurons
NeuroMem hardware platform
A typical NeuroMem-powered platform is composed of a processing unit (MCU, FPGA or both) interfaced to a bank of NeuroMem chips through the NeuroMem parallel bus or an intermediary serial communication controller. Depending on the targeted application, GPIOs and communication ports are connected to the MCU or FPGA. A Flash memory or SD card are convenient to store locally the MCU/FPGA configuration files and the NeuroMem pre-trained knowledge files if any.
The NeuroMem parallel architecture enables a seamless scalability of the network by cascading chips.
NeuroMem software tools
NeuroMem Knowledge Builders are simple frameworks to teach the neurons and validate their knowledge on training and testing datasets. Detailed reports guide you to achieve the best compromises between the accuracy and throughput. They pinpoint categories which are not properly modeled and subject to confusion and let you evaluate consolidation rules to waive uncertainties within a single network or multiple networks trained on different features. Typical diagnostics include data reduction factor, rate of convergence, accuracy per category, confusion matrices, RBF versus K-NN comparisons and more.
Harwdare driver to access the neurons through their parallel bus or other communication bus such as I2C and SPI.
Library to manipulate vector data and broadcast them to the neurons for learning or recognition. Data can derive from any data types including text, packets, measurements, images and waveforms.
Library to manipulate pixel data, extract feature vectors from regions and broadcast them to the neurons for learning or recognition. Pixel data can come from image files or image frames from a live or stored video streams.
Integrated Software Toolchain
A wealth of applications
Pass/Fail, grade quality, locate defects, classify anomalies
Locate people, measure cell density, detect obstacles
Track vehicles, people, cells under a microscope
Semiconductor inspection, QC of printed materials
- Reduce development time and costs through faster learning and targeted case studies
- Get full insight on how to deploy NeuroMem neural networks for your applications from concept to implementation