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纳米尺寸acquires AI company DeepCube for $70M

纳米尺寸, a 3D printer manufacturer with a specialism in additively manufactured electronics (AME), has announced the acquisition of machine learning companyDeepCube.

已经签署了一份最终协议,NanoDimension is set to pay DeepCube shareholders around $40 million in cash and a further $30 million in American Depositary Shares (ADSs). The ADSs will stand still for varying periods of up to three years. The transaction is expected to close in the coming week, subject to customary closing conditions being met.

Deep learning with DeepCube

At the core of DeepCube’s proprietary technology is a number of deep learning algorithms that the company uses to improve data analysis and deploy complex artificial intelligence systems. The company boasts fast and accurate deep learning model training that can significantly improve inference performance and real-time metrics. As a bonus, DeepCube’s framework can reportedly be deployed on top of any existing hardware in both datacenters and edge devices, resulting in benefits such as speed improvement and memory use reduction.

The company’s technical staff – ML experts, defense force veterans, homeland security professionals, governmental big data agencies, and academics – are all expected to join Nano Dimension once the deal closes.

The DeepCube team. Photo via DeepCube.
The DeepCube team. Photo via DeepCube.

迈向分布式数字制造

So what does Nano Dimension want with a deep learning company? It starts with the ongoingsemiconductor chip shortage面对全球。根据Nano Dimension首席执行官Yoav Stern的说法,印刷电路板市场的类似供应链问题也在徘徊,PCB制造商由于中国劳动力低下而受到更大的利润压力。

Nano Dimension希望通过开发基于ML的“分布式电子制造”网络来解决这个问题。该平台将对AMES的整个供应链进行数字控制和3D printed high-performance electronic devices(Hi-PEDs) produced using Nano Dimension’s 3D printers.

斯特恩解释说,“这个解决方案的核心DeepCube’s AI/ML/DL brain that is expected to manage a neural network of edge devices. They will self-learn and self-improve their efficacies, and self-manage and maximize yield throughout the total network. Nano Dimension machines shipping today, as well next generation devices which are under development, will be edge devices in this digital-fabrication-neural-network solution.”

Much like how personal computers went from standalone devices to nodes in local areas networks (and eventually the internet), Nano Dimension 3D printers will serve as contact points to a wider web. Nano Dimension customers will be able to 3D print as many electronic devices as they need, where they need them, with ML-optimized yields and price points.

Dr. Eli David, CTO and co-founder of DeepCube, concludes, “Over the last 18 months, DeepCube has been negotiating with a few of the world-leading integrated circuits and central processing unit (CPU) manufacturers which are still hoping to adopt its unique AI technology for dramatically improving the efficiency of deep learning deployments in the real-world. Those opportunities will be pursued as a part of Nano Dimension’s strategy.”

纳米尺寸's DragonFly LDM 3D electronics printer. Photo via Nano Dimension.
纳米尺寸’s DragonFly LDM 3D electronics printer. Photo via Nano Dimension.

Machine learning in 3D printing

Complex machine learning models have a whole host of applicatications in 3D printing, beyond just distributed workflow optimization. Metal and composite 3D printer OEM标记recently launched its ownAI-based Blacksmith softwarefor use with theX7 3D printer. Leveraging the X7’s own existing laser micrometer and a patented scanning algorithm, Blacksmith can be used for on-the-fly part inspection mid-print.

Elsewhere, at the图宾根大学, researchers recently 3D printed a robotic arm capable ofmimicking the movements of an elephant’s trunkusing machine learning algorithms. Equipped with a gripper on the tip, the FDM-printed robot can roam around and learn to adapt to new tasks, such as picking up small objects and transporting them.

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Featured image shows the DeepCube team. Photo via DeepCube.