This is the first of a series of articles exploring the benefits of Edge AI for a variety of applications.
The proliferation of digital transactions, smart phones, cyber-physical systems, and other internet connected objects in modern enterprises has led to generation of unprecedented volumes of data. Nowadays, companies are increasingly taking advantage of this data to improve the efficacy of their business processes and make better decisions via analytics. Machine learning (ML) and Artificial Intelligence (AI) algorithms play an instrumental role in collecting and analyzing enterprise data in fast, automated, and cost-efficient ways.
Most enterprise-scale ML/AI systems are deployed in conjunction with cloud computing infrastructures. This is because cloud computing eases the storage and management of large datasets, as well as the processing of many data points. Nevertheless, cloud-based AI systems are not the best choice when it comes to processing data close to the field and providing real-time performance. Likewise, cloud systems are vulnerable to significant cyber-security threats as large volumes of data are transferred from different field systems to a single cloud infrastructure.
The edge computing paradigm alleviates the latency and security limitations of cloud computing. Edge computing brings data collection and decision-making closer to the field where data are produced. In this way, it also moves computing power close to the data sources, which reduces latency. Moreover, it limits the amount of data that are transferred from the field to the cloud, which foundational for reducing data breaches and stronger data protection.
Reduced latency and increased security is the reason behind the ongoing shift of AI/ML applications from the cloud to the edge. In recent years edge AI deployments are gaining traction across various verticals. For instance, connected and autonomous vehicles leverage edge AI to reduce latency as split-second decision-making can make a huge impact on passenger safety. Similarly, across the manufacturing sectors, companies are taking advantage of ML algorithms on the edge to provide near real-time detection of machine failures and of defective products.
In general, state-of-the-art AI/ML applications needs strong security for a variety of reasons, including:
Edge computing is recognized for its ability to provide stronger security and data protection than conventional cloud deployments. Specifically, edge AI provides the following security benefits over cloud AI deployments:
Edge AI applications are usually deployed in conjunction with cloud infrastructures, as some algorithms run in the cloud, where data points from various distributed sources and edge computing processes are aggregated. Moreover, edge AI applications come at varying deployment configurations based on the placement of ML/AI processes. For instance, AI analytics functions can be run inside a microsystem (e.g., a sensor), within an edge computing cluster or gateway, or even in the cloud.
Different ML paradigms enable a wide set of edge AI deployment configurations. These ML paradigms include:
In non-trivial edge AI applications, it is possible to combine more than one of the above paradigms in a solution configuration. This provides increased versatility in specifying deployment configurations that meet stringent security and data protection needs.
The future of healthcare relies on the collection and processing of massive amounts of data from patients, including for example, data from medical and consumer devices (e.g., smart watches), clinical data, laboratory data, diagnostic devices (e.g., medical imaging), genomic databases, data about the patient’s medical history, and many more.
The analysis of this data though AI/ML algorithms is already driving significant improvements in many healthcare sectors, including prevention, diagnosis, prognosis, and treatment. For example, the analysis of clinical, genomic, imaging, and lifestyle data provides cardiologists with predictive insights on risk factors for various cardiovascular diseases. However, the regulatory approval and consumer adoption of AI/ML enhances services hinges on strong security and data protection measures.
Many state-of-the-art ML applications in the healthcare domain rely on cloud infrastructures, which creates considerable risks for data breaches and other cyber attacks. This is because large data volumes are transferred from the patients to the cloud, which makes it easier for malicious parties to tamper with healthcare data. In this context, the deployment of ML systems and algorithms at the edge of the network is becoming a good practice that alleviates such security concerns.
In an edge AI deployment, patient data is initially processed locally within edge devices like IoT (Internet of Things) gateways. Hence, patient data do not travel outside the perimeter of the network of the hospital or home care infrastructure. Nevertheless, select AI-based insights can be properly encrypted and transferred to the cloud infrastructure to enable further processing for statistical or clinical purposes (e.g., clinical trials). For example, if a patient’s respiratory sounds are to be analyzed, instead of transferring the entire recording to the cloud, edge AI may be used to infer significant events: number of coughs, sneezes, wheezes, and so on. In this way, both the volume of data that needs to be transferred to the clouds is reduced, reducing bandwidth requirements, and the patient’s data, which may include conversations is protected on the premise or within a device.
Healthcare data processing infrastructures must be compliant to applicable regulations like HIIPA (Health Insurance Portability and Accountability) in US and GDPR in Europe. This can be quite challenging given that patient information comprises personally identifiable data. Fortunately, Edge AI can help reduce the risks of data tampering and boosts regulatory compliance. Most importantly, patients and healthcare providers can choose when to share their data and for what purpose. This eases the implementation of informed consent processes that are fundamental for compliance to data protection regulations.
Edge computing can provide stronger security and data protection than conventional cloud deployments making it the right choice for security-sensitive applications such as Ip protection, healthcare, and many more. This is the first in a series of articles exploring the benefits of Edge AI across applications, industries, and products.
Syntiant combines advanced silicon solutions and deep learning models to provide ultra-low-power, high performance, deep neural network processing for edge AI applications across a wide range of consumer and industrial use cases, from earbuds to automobiles. The company’s Neural Decision Processors (NDP) are optimally designed to deploy deep (machine) learning models on the edge, where power and area are often constrained. Syntiant’s NDP solutions can equip, every device, from earbuds and doorbells to automobiles and healthcare wearables, with powerful deep learning capabilities, enabling real-time data processing and decision making, with near zero latency, enabling secure and private artificial intelligent solutions.