Deploying NeuroMem-powered intelligence

Trainable, Responsible, Explainable

Deploying a NeuroMem engine

Load

Collect data, visualize, curate, annotate with labels

Learn

Train the neurons with features extracted from annotations

Validate

Verify the classification of new datasets, identify uncertainties

Deploy

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.

NeuroMem I/Os

Harwdare driver to access the neurons through their parallel bus or other communication bus such as I2C and SPI.

CogniPat library

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.

CogniSight library

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

Inspect

Pass/Fail, grade quality, locate defects, classify anomalies

Find

Locate people, measure cell density, detect obstacles

Track

Track vehicles, people, cells under a microscope

Match

Semiconductor inspection, QC of printed materials