What is Edge Artificial Intelligence (AI) – and why is it actually needed?
‘Edge AI’ Computing is typically deployed for scenarios where collated data needs to be analysed close to the point of source of extraction; a good example would be a camera policing a high speed production line detecting for faults or anomalies during the production process. Data can be looked at in ‘real time’ (i.e. pretty much there & then) or via sending the data to a cloud based system for analysis at a later point in time.
However, Edge AI provides for added benefits especially when looked at in comparison to the traditionally used ‘Cloud’. Take, for instance, real-time data analysis where Edge AI analyses the data locally – right at the point at where its being collected. This reduces latency since the data doesn’t have to be transmitted to the cloud, (to be looked at a later point in time) and consequently facilitates for making real-time (faster) decisions. This kind of ability can be critical for self-driving cars, robots in factories, fast moving production……etc.
Since Edge AI doesn’t require a connection, it can also provide for reduced costs around data communication in comparison to Cloud AI. The same applies to power consumption as well – which is an enabler for the creation of better wearable devices that can be useful in environments such as hospitals. From a security aspect, since most of the data stays ‘local’ Edge AI supports the needed secutities around data protection. In cloud AI, the data is streamed and stored in the cloud which can potentially open the door to vulnerabilities.
Examples of where Edge AI is deployed is on autonomous robots to help the device understand & react (making calculated decisions) in the immediate environment that surrounds it. Such a device will rely on the analysis of real-time data to ensure the safety of others – the data being collated via a multitude of on-board sensors.
AIoT: When AI Meets the Internet of Things
While IoT provides data, Artificial Intelligence acquires the power to provide for responses – offering the opportunity for both creativity and the context to drive further actions; as the data is delivered and analysed with AI, businesses can make informed decisions. AI IoT succeeds in providing the following :-
- Management, analysation and provision of meaningful insights from collated data
- Fast and accurate analysis
- Localised and centralised intelligence
- Confidentiality and data privacy
- Maintenance of security against data attack
The Benefits of AI Enabled IoT
Combining IoT & artificial intelligence leads to a broad range of benefits for both companies and consumers alike – such as proactive intervention, a more personalised experience and intelligent automation. The following are some of the business benefits that can result through combining the technologies :-
Boosting Operational Efficiency
AI in IoT ‘crunches’ the constant streams of data and detects the patterns not immediately visible via more simpler methodologies. In addition, machine learning coupled with AI can predict operation conditions and detect the parameters to be modified to ensure the best outcomes. Hence, intelligent IoT offers an insight into which processes are redundant and time-consuming and which tasks can be fine-tuned to enhance efficiency.
Eliminates Costly Unplanned Downtime
In some sectors like offshore oil & gas and industrial manufacturing, equipment breakdown can result in costly unplanned downtime. Predictive maintenance with AI enabled IoT allows you to predict the equipment failure in advance and schedule orderly maintenance procedures – therefore, avoiding the side effects of downtime.
Increase IoT Scalability
IoT devices range from mobile devices and high-end computers to low-end sensors. However, the most common IoT ecosystem includes low-end sensors, which offers floods of data. An AI-powered IoT ecosystem analyses and summarises the data from one device before transferring it to other devices. As such, it reduces large volumes of data to a more easily, handleable level and allows connecting a large number of IoT devices. This is called scalability.
Better Risk Management
Pairing AI with IoT helps businesses to understand, as well as predict, a broad range of risks and automate for a prompt response. It allows them to better handle areas such as financial loss, employee safety and cyber threats.
New and Enhanced Products & Services
NLP (Natural Language Processing) is getting better at allowing people to communicate with devices. IoT and AI together can directly create new products or enhance existing products & services by enabling the business to rapidly process & analyse the data.
BVM and AI: How to bring an Edge AI solution to market
- Development kits to help test and prototype your edge AI solution are readily available, but DEV Kits are rarely suitable for use in the environment where they will ultimately be deployed.
- A purpose-built industrial-grade AI system based on Nvidia Jetson, Intel Movidius, or a high-end GPU, will be more appropriate and will offer more reliable long-term operation.
- Our edge AI systems are designed for use in environments where shock, vibration, or extreme temperatures can be a problem, and will run 24/7 for many years.
Common Applications for Edge AI
Transport and Surveillance
Artificial Intelligence (AI) is a method of using computers for perception, logic and learning. AI uses machine learning so that AI system performance improves over time and with more data analysis. This is achieved using Deep Learning algorithms based on neural networks which connect inputs and outputs in a similar way to the way brain works. Problem solving is learnt by the system itself and not hard coded by computer technologists. Deep Learning is used to Train the AI systems by providing data repeatedly to hone the system’s ability. When the AI system is trained it works to achieve the end goal which is to perform analysis or decision making.
Artificial Intelligence is achieved by using systems with a combination of high performance scalable processors such as the Intel® Xeon®; FPGAs (Field Programmable Gate Arrays); Vision Processing Units (VPUs) and Neural Network Processors (NNPs).
AI Hardware Accelerators
GPU | CPU | VPU | FPGA
An AI accelerator is a kind of specialised hardware accelerator or computer system created to accelerate artificial intelligence apps, particularly artificial neural networks, machine learning, robotics, and other data-intensive or sensor-driven tasks. They usually have novel designs and typically focus on low-precision arithmetic, novel dataflow architectures or in-memory computing capability.
NVIDIA: Jetson (GPU)
Nvidia Jetson is a series of embedded computing boards from Nvidia. The Jetson TK1, TX1 and TX2 models all carry a Tegra processor (or SoC) from Nvidia that integrates an ARM architecture central processing unit (CPU). Jetson is a low-power system and is designed for accelerating machine learning applications.
NVIDIA JetPack SDK is the most comprehensive solution for building AI applications. It bundles all the Jetson platform software, including TensorRT, cuDNN, CUDA Toolkit, VisionWorks, GStreamer, and OpenCV, all built on top of L4T with LTS Linux kernel.
JetPack provides your Jetson Developer Kit with the latest OS image, libraries and APIs, samples, and documentation, as well as developer tools.
CPU + GPU Computing
A Graphics processing unit (GPU) is a specialised chip that can do rapid processing, primarily for the purpose of rendering images. They have become a key part of modern supercomputing. They have been used in growing new hyperscale data centres and have become accelerators, speeding up all sorts of tasks – from encryption, to networking, to AI. GPUs have sparked an AI boom, become a key part of modern supercomputers, and continue to drive advances in gaming and pro graphics.
Modern GPUs are great at handling computer graphics and image processing. Their extremely parallel structure makes them more valuable than general-purpose central processing units (CPUs) for algorithms that process huge blocks of data in parallel. Multiple GPUs are utilised on supercomputers, on workstations to expedite processing multiple videos at once and 3D rendering, for VFX and for simulations, and in AI for training workloads. In contrast to a CPU, NVIDIA GPUs, for example, contain chips that have what are known as CUDA Cores, and each one of these cores is a tiny processor that can execute some code.
Intel Movidius: Vision Processing Unit (VPU)
A vision processing unit (VPU) is a rising class of microprocessor, and a particular type of AI accelerator intended to quicken machine vision tasks. The vision processing unit is reported as more fitting for performing various kinds of machine vision algorithms. These tools may be designed with particular resources for capturing visual data from cameras, and are built for parallel processing. Some of these tools are low power and high performance and may be plugged into interfaces that enable programmable use.
Vision processing units are fit for performing machine vision algorithms such as CNN (convolutional neural networks), SIFT (Scale-invariant feature transform) and other similar ones. They may include direct interfaces to take data from cameras (bypassing any off-chip buffers) and have a greater emphasis on on-chip data flow between many parallel execution units.
Field-Programmable Gate Array (FGPA)
A field-programmable gate array (FPGA) is an integrated circuit (IC) made to be configured by a customer or a designer after manufacturing, which is why it is called “field-programmable”. FPGAs include a range of programmable logic blocks and a hierarchy of “reconfigurable interconnects” that enable the blocks to be connected together like many logic gates that can be inter-wired in various configurations.
FPGAs can be beneficial over GPUs in terms of interface flexibility and enhanced by the integration of programmable logic with CPUs and standard peripherals. GPUs, on the contrary, are optimised for parallel processing of floating-point operations utilising thousands of small cores. They also provide big processing capabilities with larger power efficiency. FPGAs, which can do a wide range of logical functions simultaneously, are being considered unsuitable for emerging technologies such as self-driving cars or deep learning applications.
AIoT Ready Hardware
Like most applications that BVM meet, where IoT based projects are concerned – each application demand will differ from the next. So we can meet these differing needs, and through the support of our OEM partner channel, BVM have built (and continue to expand) a portfolio of product to scale for computing efficiencies required around both Edge based PC’s – and Edge servers to provide for reliable performance solutions. All our solutions are provided on industrial lifetime availability programs – so on a minimum of 3-5 years
Edge Servers – High Performance Computers
BVM’s edge servers put you in control of the industrial IoT solutions you’re looking to develop and deploy – allowing your application to constantly analyse right where the data is being produced.
With our edge servers, processing, information delivery, storage and IoT management can be completed ‘in situ’ saving you computational time, reducing bandwidth costs and improving latency.
AIoT Edge Devices – Low Powered, High Performance Computers
Our solutions can help with applications such condition monitoring of multiple devices for the purpose of predictive maintenance or anomaly detection in communications networks.
BVM supply systems with powerful and capable CPU’s providing processing engines that can handle several applications simultaneously.
IoT Gateways – Low Powered
Essentially, Industrial IoT gateways serve as computers that allow devices and sensors to communicate with one another, as well as communicate information to the cloud. However, IoT gateways are capable of so much more in terms of processing, memory, and storage capacity in close proximity to sensory data – and BVM have a wide ranging portfolio of gateway products to cover a multitude of computing needs.
Deep Learning Computers
Whilst GPU-accelerated hardware is a central point of deep learning and AI, it is worth understanding that the hardware requirements vary significantly depending on which stage the of the AI journey you are at – Development, Training or Inferencing. Each has very different needs and BVM recognises this by offering a range of solutions within each area to ensure every price range and performance requirement is met.
AI Accelerator Cards
BVM provide a wide range of Industrial AI accelerator cards solutions for machine vision, learning and AI applications requiring additional processing power whilst maintaining a ruggedized design. These systems typically integrate either a VPU (Vision Processing Unit), FGPA ( Field-Programmable Gate Array) or GPU (Graphics Processing Unit) and still retain the option where you can maintain a rugged design.
Motherboards and SBCs
Small Form Factor Industrial and Embedded Motherboards and SBCs. Industrial Grade Motherboards provide the backbone for Industrial PC Systems, they are revision controlled and are available for a longer time scale compared to commercial motherboard’s and typically operate over a wider temperature range than their commercial equivalents.
GPU/VPU Accelerated Computers
BVM provide a wide range of Industrial GPU accelerated solutions for machine vision, learning and AI applications requiring additional processing power whilst maintaining a ruggedized design.
These systems typically integrate either a VPU (Vision Processing Unit) or GPU (Graphics Processing Unit) and still retain the option where you can maintain a fanless design.
AI Ready Panel PCs
AI-powered imaging applications require a suite of enabling technologies. First and foremost, processors equipped with HD graphics features and hardware-accelerated video encoding/decoding are a must. These capabilities are available on a selection of our Panel PC compute devices that are equipped with Intel Core processors. Deployments already exist in a wide array of medical imaging applications such as ultrasounds, X-rays, MRIs, and CT scans – where scalable processors can efficiently perform deep-learning inferencing and thanks to a hybrid CPU-plus-GPU architecture that supports complex, memory-intensive medical imaging workloads.
AI Development Kit
BVM provide starter kits to allow users to unleash the power of modern artificial intelligence solutions to allow the development of understanding and skills around visual processing units (VPU) and dedicated hardware accelerators for running on-device deep neural network applications. We’re also here to support you where a guiding hand is needed.
We like to make life easier ….
Our technical & commercial team members will always provide you with valuable but impartial advice around the products and services that BVM supplies. With their collective backgrounds, they’ll provide you with the benefit of their knowledge and experiences when & where you need it. We’ll always help you in the first instance and get back to you when additional information is required.