Fully integrated within Axis end-to-end solutions, AXIS A1210 Network Door Controller is optimized for both small and large installations. This smart door controller is a compact, lightweight barebone unit that offers easy installation in areas where space is limited, with a smaller footprint design than previous Axis door controllers. It includes a keyhole mounting plate and support for DIN rail mount for fast and easy installation on walls. Plus, with plenum rating, it’s suitable for installation in plenum spaces and eliminates the need for an additional installation cabinet.

Featuring two reader connections as well as a Form C relay to power the lock, AXIS A1210 includes everything a user needs to control one door. In addition, it’s all powered using one Power over Ethernet (PoE) cable, making installation and maintenance a breeze. And thanks to intelligence on the edge, this smart door controller can internally handle all tasks related to door access – even if the network is down. AXIS A1210 also offers built-in cybersecurity features to prevent unauthorized access and safeguard the system from attack. This includes Axis Edge Vault, which protects the Axis device ID and simplifies authorization of Axis products on the network. Furthermore, it ensures cryptographic keys are safely and securely stored in the onboard EAL6+ Certified compute module.

This scalable solution is designed to meet users’ changing needs as they develop. Users can store up to 250k credentials and 250k event logs on the device itself, and it ensures flexible and secure authentication using different types of credentials. For instance, users can grant access to thousands of visitors using traditional access types such as card or PIN codes, dynamic QR codes, and license plate verification for vehicle access. 

Unlike in image processing or large language models, few AI startups are focused on sequential data processing, which includes video processing and time-series analysis. BrainChip is just fine with that.

With all the buzz around LLM generative AI, it is understandable that other forms of AI seem to have vaporized in the ChatGPT mist. One often overlooked area is analyzing time-series data, such as streaming stock quotes and video processing. BrainChip has singled out such data processing needs as a critical opportunity to apply its Akida technology, which specializes in Event-Based Neuromorphic processing of ViT, CNN, TENN, and RNN. Let's look at BrainChip's ability to perform well at low power in this emerging market.

Sequential Data Analysis

Sequential analysis refers to the process of analyzing and extracting insights from data that is collected and organized in chronological order. This data type typically involves measurements or observations taken at regular intervals over time. Time-series analysis techniques aim to understand data patterns, trends, and dependencies and make predictions or forecasts based on historical patterns.

Use cases of sequential data analysis include financial analysis, demand forecasting, predictive maintenance, energy consumption analysis, and IOT sensor data analysis. The market size for applying AI in time-series data analysis is growing as organizations recognize the value of extracting insights and making accurate predictions from temporal data. While specific market size figures for this realm are not readily available, the broader AI market, including applications in time-series analysis, is expected to grow substantially. According to a report by Grand View Research, the global AI market size was valued at USD 62.35 billion in 2020 and is projected to expand at a compound annual growth rate (CAGR) of 40.2% from 2021 to 2028. This growth encompasses various AI applications, including time-series analysis, across multiple industries.

Adding the wrinkle of Time into Neural Networks

Traditional Convolutional Neural Networks (CNNs) have been around for 30+ years and combine multiple hidden layers trained in a supervised manner. These are sequential in nature and hence referred to as feed-forward neural networks. Bi-directional networks, also called Recurrent Neural Networks (RNNs), invented at the turn of the century, added capability for more complex learning such as language modeling. But for applications to time series, a machine learning engineer needed a combination of CNNs and a temporal network for spatial-temporal analysis. While academia developed networks that did temporal convolution, none have been power efficient or easy to train to make it to the far Edge.

TENNs: Temporal Event-based Neural Networks

Enter Temporal Event Based Neural Nets (TENNs). BrainChip, the first company to commercialize neuromorphic or event-based processing IP, has extended this to efficiently combine spatial and temporal convolutions to process sequential data in an innovative approach. A significant benefit is that it overcomes the training complexity and trains just like the simpler CNNs, but with the added benefit of reducing models' size without losing accuracy. All of this leads to improved performance and greater efficiency in the execution of complex models, which is imperative for Edge AI devices.

The advantages of using TENNs (Temporal Event-based Neural Networks) for analysis include:

· They learn to represent the temporal structure of the data, which can be essential for tasks such as forecasting and anomaly detection.

· They can make predictions for future time steps.

· They can be trained on large datasets of time series data.

Overall, TENNs are a powerful tool for processing time series data. They can learn to represent the temporal structure of the data and make predictions for future time steps. But TENNs go further and start treating streaming inputs like video like a time series of frames, performing a 3D convolution comprising of a temporal convolution on the time axis and a spatial convolution on the XY axis. The secret Brain chip claims is the efficient way they achieve this convolution, enabling more advanced, higher resolution video object detection in tens of milliwatts.

The Akida 2^nd generation Processor and TENNs

The BrainChip Akida processor is inspired by the energy-efficient way of the human brain's functionality. Akida, unlike traditional neuromorphic approaches, which are analog, is a fully digital portable processor IP that can perform tasks such as image classification, semantic segmentation, and odor recognition, including time-series analysis. It supports most current neural network architectures in addition to TENNs

The Akida processor uses unique, highly parallel event-based neural processing cores. It has innovatively merged neuromorphic processing with native support for more traditional convolutional capabilities and functions, including hardware support for TENN networks. Its neuromorphic processing cores communicate using sparse, asynchronous events. This event-based processing method is well-suited for time-series data analysis because it efficiently manages high-speed, asynchronous, and continuous data streams.

BrainChip’s Akida brings this innovative ability to look at vision, video, and other three-dimensional data as time series. An object visible through multiple two-dimensional frames, computed with the time element as the third dimension, makes video object detection much more effective. Akida’s support for efficient spatial-temporal convolutions makes this use case significantly faster with lower energy consumption

The foundations of the Akida Processor.

The processor features a high-speed, low-power digital design optimized for edge computing applications, enabling real-time processing and low-latency analysis. Akida can process structured and unstructured data and learn and recognize patterns from streaming data, which is necessary for time-series data analysis.

Akida provides a traditional CNN accelerator and adds TENN and Vision Transformer logic for a more comprehensive solution to sequential processing. The Akida processor is particularly effective for real-time data classification, anomaly detection, and predictive analytics. Consequently, the second-generation Akida processor, with TENNs support, is designed to provide efficient and accelerated hardware solutions for not just traditional one-dimensional (1D) time-series analysis tasks and various types of signals but takes the time-series paradigm to multi-dimensional applications like video object detection and vision using the underlying event-based processing paradigms

Conclusions

TENNs have demonstrated state-of-the-art performance across various domains of sequential data. TENNs offer superior performance with a fraction of the computational requirements and significantly fewer parameters than other network architectures. This efficiency makes them an elegant solution for highly accurate models that support video and time series data at the Edge. TENNs looks extremely attractive for processing raw signal data, which it can consume directly without needing DSP/filtering, enabling exceptionally compact audio management applications, including denoising. The MetaTF tools that plug into existing frameworks like TensorFlow and formats like ONNX simplify model evaluation, development, and optimization. (MetaTF is a free obtain from the BrainChip website here ).

The second-generation Akida processor IP is available now from BrainChip for inclusion in any SoC and comes complete with a software stack tuned for this unique architecture. We encourage companies to investigate this technology, especially those implementing time series or sequential data applications. Given that GenAI and LLMs generally involve sequence prediction, and advances made for pre-trained language models for event-based architectures with SpikeGPT, the compactness and capabilities of BrainChip’s TENNs and the availability of Vision Transformer in second generation Akida could facilitate more GenAI capabilities at the Edge.

For more information on TENNs and BrainChip Akida, see our white paper at:

To say Mars is a bizarre planet might be something of an understatement. It has nearly no atmosphere, has an unstable liquid metal core that causes it to wobble on its axis constantly, and as a frozen desert, is an oxymoron in itself. As if Mars wasn’t strange enough, data from NASA’s InSight Lander (RIP) has now revealed that the red planet is spinning faster and faster every year.

The increasing spin went unknown until a research team found evidence of acceleration through InSight’s RISE (Rotation and Interior Structure Experiment) instrument. That same team, led by radio scientist Sebastien Le Maistre of the Royal Observatory of Belgium, who is also the principal investigator of RISE, had previously found that the core of Mars is most likely a glob of molten metal. Looking further into RISE data from InSight’s first 900 days on Mars, they saw that the planet’s spin was accelerating by a fraction of a millisecond per (Earth) year, or about 0.76 milliseconds. Martian days are gradually growing shorter. But why?

What lies beneath—or above

RISE’s main objective was to see how much Mars wobbled as its orbit was pushed and pulled by the gravity of the Sun. This would determine whether the core was more likely to be solid or liquid. However, RISE also had another task, which was measuring the length of a Martian day. Days on Mars, known as sols, are about a half-hour longer than Earth days at 24 hours and 37 minutes. RISE measured both the rotation rate and wobbling of Mars with reflected radio waves. When it received a radio signal from NASA’s Deep Space Network (DSN), it would reflect those waves right back at Earth. The difference between the frequency of the signal sent out by the DSN and the signal that bounced back to Earth told the InSight team how the lander was moving along with Mars.

Changes in the frequency of reflected radio waves revealed both wobbles in orbit and how long a day lasted on the red planet, and RISE measured changes in day length more precisely than ever, with five times more accuracy than the Viking landers had. There was also another way RISE found evidence that Martian days were getting slightly shorter. It also tracked shifts in carbon dioxide at the poles, where CO₂ will sublimate as the planet warms up in the spring and summer or condense as the planet cools down in the fall and winter.

Ice, ice baby

While we know why Earth’s rotation has been slowing down over billions of years and making our days longer, scientists are not positive about the exact reason the spin of Mars is accelerating and shortening its days. But there's a reasonable chance that it has something to do with changes in the red planet's ice caps.

When Martian ice caps lose carbon dioxide ice to sublimation during the warmer months, regions that had been covered become mostly ice-free. Le Maistre and his team suggest that post-glacial rebound or ice accumulation (or both) would bring the mass of Mars closer to its axis as it rotates, though in different ways. It can happen when carbon dioxide from the atmosphere condenses into ice that accumulates at the polar ice caps, which are extremely close to the axis. Alternatively, post-glacial rebound deforms the planet as ice sublimates and land mass moves back into the gaps left behind.

Alternatively, the researchers think it is possible for Mars to accelerate through core-mantle coupling, which involves momentum from its liquid core being transferred to the mantle.

“Evidence of a slow acceleration in the Martian rotation rate [could] be the result of a long-term trend either in the internal dynamics of Mars or in its atmosphere and ice caps,” they said in a study recently published in Nature.

Finding such minuscule variations in the data was a tedious undertaking. The researchers needed to wait a painfully long time for enough data to come in while the lander was still operational, and when they finally had that data, they needed to rule out all possible causes of noise that could interfere with their results, such as water and the solar wind, both of which could have slowed down the reflected radio signals from InSight that were traveling to Earth from Mars.

Even though InSight met its demise in a dust storm, Le Maistre hopes to keep learning more from the data and potentially determine the culprit behind days on Mars growing shorter.

Nature, 2023.  DOI: 10.1038/s41586-023-06150-0

BEIJING, Aug. 22 (Xinhua) -- For Eleanor Grace Ellsworth, a college student from the United States, her recent experience of cycling across the Central Axis of Beijing was just like a time travel trip which brought her into close contact with the rich history and culture of the Chinese capital.

"It has been really fascinating! This class was a good opportunity to actually explore the city," said Ellsworth, from Rhodes College in Memphis.

Ellsworth was among a group of students from different countries who joined a cycling course offered by the Peking University Summer School International 2023. During the course, which lasted a month, students had the opportunity to gain cycling knowledge and skills, while joining cycle tours to explore the ancient charms of Beijing.

"We hope that students from all over the world can experience the long history of the ancient capital, including the grandeur of royal architecture, and explore the beauty of the city from different perspectives," said course lecturer Lu Fuquan, who is an associate professor.

A total of 51 students from seven countries and regions, including China, Sweden, Australia and Malaysia, have joined the course.

According to Lu, six cycling routes were designed to enhance students' exploration of the historical beauty of the city. These routes not only include popular tourist sites such as the Forbidden City, the Temple of Heaven and the Jingshan Park, but also feature places such as museums, theaters and former residences of celebrities.

The Central Axis of Beijing formed a major part of these cycling tours. First created in the Yuan Dynasty (1271-1368), the Beijing Central Axis, or Zhongzhouxian, stretches 7.8 km between the Yongding Gate in the south of the city and the Drum Tower and Bell Tower in the north. Most of the major old-city buildings of Beijing sit along this axis.

The students visited more than 10 important sites along the Central Axis. Neither the recent high temperatures in the city, nor the occasional rainy weather, managed to reduce the enthusiasm of the students, who kept on riding wearing either sun hats or raincoats.

Ellsworth, who has been learning Chinese for six years but had never previously set foot in China, was fascinated by these historical sites. "I've read about them in books and I have heard about various dynasties, but it's another thing to actually walk in these historical places, and to just see that they are still there and people still care about them."

Gabriel Barnagaud, an art history major from Ecole du Louvre in France, said he gained a lot of interesting knowledge during the cycling course, especially by visiting museums and venues with different types of architecture.

"The Forbidden City is probably one of my favorite Beijing venues, and the whole area around it is full of wonderful sceneries and buildings," he said. "The Central Axis offers lots of important things along its long way. It's really impressive and I feel very small compared to it."

Jiang Yatong, a student from the National University of Singapore, believes that cycling is like a "moveable feast," and found that the image of Beijing became increasingly profound as the cycling course went on. The colors of ancient architecture and little sculptures of animals visible on the roofs of palaces all made a strong impact on her, "as if every brick and every tile have their own stories."

At the end of the course, Ellsworth began to look forward to her next visit to China. "I plan on coming back at some point in the future, because I feel like even if I came here like 30 or 40 times, and I spent months here every time, I still wouldn't have seen everything," she said.

(Interns Liu Yifeng and Xiao Zehui also contributed to the story.)

Plot Summary: Italy, once home of the strongest empire in the world, has changed. This is the story of Italy, now a cowardly pasta-loving fool, and his friends (other personified countries of the world) as they live through strange moments in world history.

Unleashing the Power of Microfluidic Picodroplets

Richard Hammond

In this interview conducted at SLAS EU 2023 in Brussels, Belgium, we spoke to Richard Hammond, Chief Technical Officer at Sphere Fluidics, about Sphere Fluidic's microfluidic picodroplet technology and more.