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Today's technology landscape is undergoing a fundamental shift toward edge computing, driven by rapid advances in generative ai (GenAI) and traditional ai workloads. Historically dependent on cloud computing, these ai workloads are now encountering the limits of cloud-based ai, including concerns about data security, sovereignty, and network connectivity.
To address these limitations of cloud-based ai, organizations are looking to embrace edge computing. Edge computing's ability to enable real-time analytics and responses at the point where data is created and consumed is why organizations see it as critical to ai innovation and business growth.
With its promise of faster processing with zero to minimal latency, cutting-edge ai can dramatically transform emerging applications. While the computing capabilities of edge devices are getting better and better, there are still limitations that can make it difficult to deploy highly accurate ai models. Technologies and approaches such as model quantization, imitation learning, distributed inference, and distributed data management can help remove barriers to more efficient and cost-effective edge ai deployments so organizations can realize its true potential.
ai inference in the cloud is often affected by latency issues, causing delays in data movement between devices and cloud environments. Organizations are realizing the cost of moving data between regions, to the cloud, and back and forth from the cloud to the edge. It can hinder applications that require extremely fast, real-time responses, such as financial transactions or industrial security systems. Additionally, when organizations must run ai-powered applications in remote locations where network connectivity is unreliable, the cloud is not always within reach.
The limitations of a “cloud-only” ai strategy are increasingly evident, especially for next-generation ai-powered applications that demand fast, real-time responses. Issues such as network latency can slow down the information and reasoning that can be delivered to the cloud application, resulting in delays and increased costs associated with transmitting data between the cloud and edge environments. This is particularly problematic for real-time applications, especially in remote areas with intermittent network connectivity. As ai takes center stage in decision-making and reasoning, the physics of data movement can be extremely costly and negatively impact business outcomes.
Gartner predicts that more than 55% of all data analysis using deep neural networks will occur at the point of capture in an edge system by 2025, up from less than 10% in 2021. Edge computing helps alleviate latency , scalability, data security, connectivity and more challenges, reshaping the way data processing is handled and, in turn, accelerating the adoption of ai. Developing applications with an offline approach will be essential for the success of agile applications.
With an effective edge strategy, organizations can get more value from their applications and make business decisions faster.
As ai models become increasingly sophisticated and application architectures become more complex, the challenge of deploying these models to computationally constrained edge devices becomes more pronounced. However, advances in technology and evolving methodologies are paving the way for the efficient integration of powerful ai models within the framework of edge computing, ranging from:
Model compression and quantization
Techniques such as model pruning and quantization are crucial to reducing the size of ai models without significantly compromising their accuracy. Model pruning removes redundant or non-critical information from the model, while quantization reduces the precision of the numbers used in model parameters, making models lighter and faster to run on resource-constrained devices. Model quantization is a technique that involves compressing large ai models to improve portability and reduce model size, making them lighter and suitable for edge deployments. Using fine-tuning techniques, including generalized post-training quantization (GPTQ), low-rank adaptation (LoRA), and quantized LoRA (QLoRA), model quantization reduces the numerical precision of model parameters, making make models more efficient and accessible for edge devices such as tablets, edge gateways, and mobile phones.
Edge-Specific ai Frameworks
Developing ai libraries and frameworks designed specifically for edge computing can simplify the process of deploying edge ai workloads. These frameworks are optimized for the computational limitations of edge hardware and support efficient model execution with minimal performance overhead.
Databases with distributed data management
With capabilities such as vector search and real-time analysis, help meet edge operational requirements and support local data processing, handling various types of data such as audio, images, and sensor data. This is especially important in real-time applications, such as autonomous vehicle software, where various types of data are constantly being collected that must be analyzed in real time.
Distributed inference
Placing models or workloads on multiple edge devices with local data samples without actual data sharing can mitigate potential data privacy and compliance issues. For applications, such as smart cities and industrial IoT, that involve many IoT and edge devices, it is critical to consider distribution inference.
While ai has been predominantly processed in the cloud, finding a balance with the edge will be critical to accelerating ai initiatives. Most, if not all, industries have recognized ai and GenAI as a competitive advantage, which is why quickly gathering, analyzing, and deriving insights at the edge will become increasingly important. As organizations evolve their use of ai, implementing model quantification, multimodal capabilities, data platforms, and other cutting-edge strategies will help drive meaningful, real-time business results.
Rahul Pradhan is Vice President of Product and Strategy at Couchbase (NASDAQ: BASE), provider of a leading modern database for enterprise applications relied on by 30% of Fortune 100 companies. Rahul has over 20 years of experience leading and managing teams. of engineering and products focusing on databases, storage, networking and cloud security technologies. Prior to Couchbase, he led the product management and business strategy team for Dell EMC's Midrange Storage and Emerging Technologies divisions to bring all NVMe, Cloud, and SDS flash products to market.
