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Speed up your Machine Learning

Fixstars' machine learning solution

What is Machine Learning

Machine learning is a technique that aims to realize a learning function similar to a human being on a computer, and it is advanced by finding out the regularity and knowledge hiding behind large amounts of data and generalizing it. Along with the drastic improvement in hardware performance, environments that can process "big data" used for learning quickly, in large quantities and at low cost, began to be developed, and the use is expanding rapidly for various fields such as natural language processing .

"Deep learning" has been emerging. Deep learning is a kind of machine learning method with multiple layers of weighting coefficients that simulate a "neural network". Effectiveness became widely recognized by showing surprising results in benchmark tests such as image recognition. During these performance improvements, there has been an evolution of hardware that supports high-speed processing of large amounts of data, such as GPU and CPU.


With increasing attention to machine learning, it is now being used in more sophisticated applications. The amount of data used is increasing explosively and the demand for high-speed processing is expanding day by day. In addition, there are many cases where machine learning is under severe performance constraints such as autonomous driving and Industry4.0. It is an era in which it is essential to optimize the usage conditions.

Fixstars' machine learning solution

Machine learning acceleration

Smarter and smarter with larger data processing


We focus on image processing, a major area where deep learning first showed promising results. We are working on accelerating machine learning in various fields such as estimating stock prices and real estate prices in finance and improving the yield of manufacturing processes in industry.

We research the latest articles and reports, and then propose and develop machine learning algorithms suitable for the applications requested by the customer, and implement them for the customer's environment.

We optimize software with your targeted application and hardware environment. This includes not only GPUs which are often used in machine learning but also SoCs and other hardware environments.

Project improvement platform by AI "helmi"

Predicting defects and shortening lead time to fix bug


As software systems becomes larger and more complicated, development costs get larger. Through software testing and quality control, we estimate the defect location and improve the efficiency of the verification process based on the experience of skilled technicians.
As the sophistication of software development systems grow, the number of companies that incorporate these automatic modification methods in their development environment is increasing. This is due to the evolution of hardware that enables large-scale data processing with machine learning techniques such as deep learning.

"Helmi" is a software platform that acquires huge amounts of information generated by daily software development and automatic defect estimation by machine learning.
It periodically extracts feature from defect correction history, creates machine learning models, and uses it for failure prediction.
By utilizing the estimated failure probability for software test and code review prioritization, defects can be repaired early and cost of testing can be reduced.

Source code quality
Code quality is guaranteed to some extent and "mild bugs" are reduced
Bug remodeling lead time
Early detection and early repair in the development process possible
Visualization of quality situation
Since it can be easily monitored from the development process, it is possible to respond quickly to quality improvement
Quality improvement measures
To concrete and direct correspondence rather than a personal judgment
Developer consciousness
In order to focus on development with confidence
Retirement risk
The handover of knowledge experience decreases, the risk of retirement drops
Test quality
Unnecessary test implementation, phenomenon of omission

Related Solutions

We offer various solutions related to machine learning.



Solve the problem of analog data analysis with edge computing

Olive@Factory is a solution for Factory IoT that realizes predictive maintenance of production sites.

Utilizing machine learning, grasping signs of equipment malfunction and abnormality, predicting failure. By omitting extra maintenance, maintenance cost can be reduced and equipment availability can be improved.

Related business areas

We are offering the optimum speed-up solution for customers' products in various industrial fields where high-speed processing of large amounts of data is required.

Flash storage

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Industrial equipment

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