What do these applications have in common? They all rely on the cloud to handle AI workloads. Despite the high costs of cloud-based generative AI, the nearly unlimited memory and power capacity it offers means that cloud-based applications will continue to drive the development of these popular generative AI applications.
However, what concerns many design engineers, embedded engineers, and data scientists even more is the explosive growth of edge AI applications. Performing complex generative AI tasks on edge devices presents many new challenges, such as:
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Real-time processing requirements
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Strict cost constraints
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Limited memory resources
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Compact space requirements
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Mandatory power consumption budgets
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Challenge 1: Real-Time Processing Requirements
One of the biggest drawbacks of cloud AI is the latency involved in sending and receiving data. For applications like ChatGPT, this is not an issue, as users do not notice the few seconds of delay and can wait for text generation to complete. However, in many edge applications, this delay is unacceptable.
For example, in autonomous vehicle applications, making steering and braking decisions based on images of detected people and other vehicles is crucial. Waiting for communication delays with the cloud is completely unacceptable. Edge AI applications must be able to process data in real time at the edge where it is collected to meet the requirements of these time-sensitive applications.
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Challenge 2: Strict Cost Constraints
For companies with a large customer base, using expensive cloud solutions to handle their AI workloads is financially justifiable. However, in most edge applications, cost is a critical factor and a key to building competitive products in the market. Effectively increasing generative AI capabilities within reasonable cost limits will be a challenge for AI designers and data scientists.
For instance, in smart city applications, while there is a desire to add advanced features to cameras and power monitoring systems, these features must remain within tight government budgets. Therefore, cloud solutions become impractical. Low-cost edge AI processing is essential.
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Challenge 3: Limited Memory Resources
Another advantage of cloud AI is its nearly unlimited memory capacity, which is particularly useful for processing large datasets required for precise text analysis and generation. However, edge applications do not have this luxury, as their memory capacity is constrained by size, cost, and power consumption. Under these resource-limited conditions, maximizing bandwidth and efficiently utilizing memory becomes crucial for edge applications.
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Challenge 4: Compact Space Requirements
Data centers are located in spacious environments, so these AI applications do not frequently encounter space issues like edge designs do. Whether it’s the limited under-dash space in automotive designs or size and weight considerations in aerospace applications, edge designs often face spatial constraints when implementing AI functionalities.
These designs must meet specific size requirements for computing and memory resources to efficiently process AI workloads at the edge.
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Challenge 5: Mandatory Power Consumption Budgets
Power consumption budgets pose the final and perhaps most critical challenge faced by edge designs. AI cloud consumes a significant amount of power, but since it can be directly connected to power sources, this ultimately comes down to power costs.
In edge AI applications, power may be readily available. However, power consumption remains a critical factor, as it can account for a significant portion of the overall cost of the application. In battery-powered edge AI applications, the importance of power consumption is even more pronounced, as high-power devices can shorten product lifespan, making it a critical consideration.
Solutions to Edge AI Design Challenges
Therefore, the question becomes: “How can engineers address these challenges while achieving the required functionalities and developing successful edge AI products?” Here are five key points to consider:
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Recognizing the Limitations of Edge GPUs
GPUs have many advantages in cloud AI applications, as their characteristics align well with the demands of AI cloud applications. GPUs offer powerful processing capabilities that cloud AI applications can fully leverage, and they can meet real-time processing requirements effectively.
However, when considering using GPUs for edge applications, it is important to examine the weaknesses of using GPUs. The cost of GPUs is higher than that of AI accelerators, typically ranging from 5 to 10 times more, making them unsuitable for edge AI cost requirements. GPUs are often housed in relatively large packaging and boxes, which may not meet the size requirements of edge applications. Finally, the power consumption of GPUs is significantly higher than that of typical AI accelerators, making it difficult to meet power consumption budgets.
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Recognizing the Necessity of Complete Software Solutions
Historically, many AI accelerator developers initially focused on chip design, which is understandable. This remains an important part of high-quality edge AI solutions. However, since these solutions must create and modify software to fit predetermined chip designs, they may not fully leverage all aspects of efficient AI processing.
Unlike the past, where most designers in engineering design companies focused on hardware, the number of software engineers now far exceeds that of hardware designers, making software capabilities and usability increasingly important. An excellent overall AI solution requires software development that allows the chip to process AI models as efficiently as possible. Designing the software first and then the chip will yield the best AI solution.
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Seamless Integration into Existing Systems
While some AI designs may need to start from scratch, more often, designers are trying to integrate AI capabilities into existing systems. By adding AI processing capabilities, these existing systems can provide more functionality to end users while maintaining the same physical dimensions.
The importance of being able to integrate seamlessly with current designs cannot be overstated. Therefore, providing AI solutions that offer heterogeneous support for a wide range of existing processors will make it easier to add AI capabilities to current designs.
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Efficient Neural Network Processing
Ultimately, the overall effectiveness of an AI solution depends on whether the neural network can efficiently and accurately process AI workloads. While filtering through numerous vendor offerings can be challenging, understanding the structure and capabilities of the “core” of AI solutions is crucial. We need to understand how models are processed, what innovations have been implemented to enhance processing efficiency, and how specific models compare in processing performance.
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DRAM Capacity and Bandwidth
One often-overlooked aspect of AI design is the ability of the solution to effectively utilize memory. Some accelerator solutions have limited or no on-chip memory, which can hinder AI processing capabilities. Accessing off-chip memory resources is very time-consuming and can significantly increase latency.
Other solutions may have on-chip memory, but if the DRAM bandwidth is not high enough, access latency will also limit AI processing capabilities.
Finding the Right Edge AI Solution Provider
As AI migrates from the cloud to the edge and is increasingly applied across various industries, embedded designers face numerous challenges. Choosing the right AI hardware provider that can support current and future edge AI solutions is crucial for the successful implementation of products.
Source: TechSugar
Reviewed by: Zhao Lixin
