Imaging AI

"Human behavior" recognition technology that realizes both high-accuracy recognition and high-speed processing

In the category of "human behavior", Konica Minolta develops AI algorithms of human recognition, attitude estimation, behavior recognition, and so on using Deep Learning. We develop robust "human behavior" recognition technologies that do not make recognition errors in any environment through the learning of a large number of site images. We have realized high-accuracy recognition and high-speed processing in human recognition and 2D attitude estimation for single-lens still images, and they are already used in various projects of manufacturing, retail, and healthcare applications.

"Advanced medical care" technology that extracts and visualizes movements

The "Advanced medical care" technology of Konica Minolta recognizes, extracts, and visualizes movements and signal changes of tissues captured in medical videos. This technology can extract various movements in human bodies by combining domain knowledge, signal processing technologies, and machine learning. We are contributing to medicine by quantifying changes of movements, which has relied on experience and been overlooked because of subtlety. These technologies are used in X-ray kinetic analysis systems for the measurement of lung field movement as well as the visualization and quantification of biophysiology functions related to the respiratory and circulation organs.

"Inspection" technology that detects various types of defects

The "inspection" technology of Konica Minolta can detect various types of defects and determine their locations and types in testing. The algorithms are highly customizable, so users can perform learning by themselves, adapt algorithms to their specific judgment needs, and perform learning with good images for bad image detection.
The algorithms are used for the recognition of good and bad items in digital manufacturing, automatic judgment of types of defects in automobile painted surfaces, and so on.

"NNgen", a high-level synthesis complier for FPGA

In collaboration with Associate Professor Shinya Takamaeda, The University of Tokyo, Konica Minolta developed a high-level synthesis complier "NNgen (pronounced "en-en-jen") that enables handy FPGA (Field Programmable Gate Array) implementation of a model that went through Deep Learning. NNgen is publicly available as open-source software. Using NNgen, engineers and designers without much knowledge of hardware tuning can efficiently develop a fast accelerator that runs on FPGA based on a model that went through Deep Learning.

With this, they can realize products and services that make real-time AI processing on edge devices equipped with FPGA. Because NNgen is open-source software, users can use it for free to contribute to development. NNgen is available at GitHub.
NNgen: high-level synthesis complier for Deep Learning accelerator

Development history and technology overview

Among AI technologies, Deep Learning is attracting a lot of attention. Deep Learning brought a dramatic improvement of accuracy in various fields including image processing and voice processing. On the other hand, computation of Deep Learning requires enormous computer resources, highlighting the importance of special power-saving high-performance hardware. As such special hardware, "FPGA" can be used, which is a type of hardware whose circuit configuration can be changed. Taking advantage of FPGA that the circuit configuration for required processing can be customized, you can configure the circuit to tailor it to a learned Deep Learning model to implement a compact and high-speed accelerator. NNgen is a domain-specific extensible high-level-synthesis complier used for handy and efficient FPGA implementation of a special accelerator that speedily runs a Deep Learning model that went through learning and configuration for its use purpose. NNgen generates a model-specific hardware description (Verilog HDL) of a hardware accelerator and an IP core setting file (IP-XACT).

Main features of "NNgen"

1. Handy implementation with high abstraction

• As it is with general Deep Learning frameworks, NNgen generates an accelerator circuit description automatically from a Python model structure description.
• NNgen also supports ONNX inputs.
• Performance can be optimized just by hardware parameter setting.

2. Compiler functions can be extended with a range of granularity levels.

• As the compiler backend, "Veriloggen", a multi-paradigm high-level-synthesis framework developed by Associate Professor Shinya Takamaeda is used to enable extension such as the addition of a custom layer.

3. Portability for various FPGA environments

• NNgen directly generates a Verilog HDL description from a learned Deep Learning model.

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