What are the most common AI hardware design challenges?

1 Architectural choices

One of the first challenges in AI hardware design is choosing the right architecture for the target application and workload. Different AI algorithms and models may have different requirements and characteristics, such as data size, parallelism, memory access patterns, and computational intensity. For example, convolutional neural networks (CNNs) are widely used for image recognition and processing, but they require a lot of multiply-accumulate (MAC) operations and memory bandwidth. Therefore, hardware architectures that can support high MAC throughput and efficient data movement are preferred for CNNs. On the other hand, recurrent neural networks (RNNs) are often used for sequential data, such as speech and text, but they have more complex dependencies and feedback loops. Therefore, hardware architectures that can handle variable data lengths and dynamic control flow are more suitable for RNNs. There is no one-size-fits-all solution for AI hardware design, and computer engineers have to consider various trade-offs and constraints when selecting or developing the optimal architecture.

2 Hardware acceleration

Another challenge in AI hardware design is how to accelerate the execution of AI algorithms and models on the available hardware platforms. Hardware acceleration refers to using specialized hardware components or devices to speed up certain tasks or functions that are otherwise slow or inefficient on general-purpose hardware. Hardware acceleration can improve the performance, power efficiency, and functionality of AI applications. However, hardware acceleration also introduces new challenges, such as compatibility, programmability, scalability, and flexibility. For example, GPUs are widely used as hardware accelerators for AI, but they have limitations in terms of memory capacity, communication latency, and power consumption. Therefore, computer engineers have to design and optimize hardware accelerators that can match the specific needs and characteristics of AI applications, as well as integrate them seamlessly with other hardware components and software layers.

3 Hardware verification

A third challenge in AI hardware design is how to verify the correctness and reliability of the hardware components and systems that run AI algorithms and models. Hardware verification is the process of checking and testing whether the hardware meets the specifications and requirements of the intended functionality and behavior. Hardware verification is crucial for ensuring the quality and safety of AI applications, especially for critical domains such as healthcare, transportation, and security. However, hardware verification is also very complex and time-consuming, especially for AI hardware that involves large-scale parallelism, randomness, approximation, and adaptation. Therefore, computer engineers have to develop and apply effective and efficient methods and tools for hardware verification, such as formal methods, simulation, emulation, prototyping, and debugging.

4 Hardware security

A fourth challenge in AI hardware design is how to protect the hardware components and systems that run AI algorithms and models from malicious attacks and threats. Hardware security is the aspect of ensuring the confidentiality, integrity, and availability of the hardware resources and data that are involved in AI applications. Hardware security is important for preventing and mitigating various risks and damages, such as data leakage, data tampering, data poisoning, model stealing, model inversion, adversarial attacks, and denial-of-service attacks. However, hardware security is also very challenging and demanding, especially for AI hardware that operates in diverse and dynamic environments, interacts with multiple and unknown parties, and faces sophisticated and evolving attackers. Therefore, computer engineers have to design and implement hardware security mechanisms and solutions, such as encryption, authentication, verification, isolation, monitoring, and recovery.

5 Hardware evolution

A fifth challenge in AI hardware design is how to enable and support the evolution and adaptation of the hardware components and systems that run AI algorithms and models. Hardware evolution is the aspect of allowing and facilitating the hardware to change and improve over time, in response to the changes and demands of the AI applications and environments. Hardware evolution is desirable for enhancing the performance, efficiency, functionality, and robustness of AI applications, as well as for enabling new and emerging AI capabilities and scenarios. However, hardware evolution is also very difficult and costly, especially for AI hardware that has to cope with complex and uncertain dynamics, trade-offs and conflicts, and feedback and interactions. Therefore, computer engineers have to design and employ hardware evolution techniques and strategies, such as reconfiguration, customization, optimization, learning, and self-organization.

6 Hardware innovation

A sixth challenge in AI hardware design is how to generate and explore new and novel hardware concepts and solutions that can advance and transform the field of AI. Hardware innovation is the aspect of creating and discovering hardware that can offer new and superior performance, efficiency, functionality, and intelligence for AI applications. Hardware innovation is essential for pushing the boundaries and frontiers of AI, as well as for solving the existing and emerging problems and challenges in AI. However, hardware innovation is also very challenging and risky, especially for AI hardware that has to overcome the limitations and drawbacks of the current and conventional hardware paradigms, technologies, and architectures. Therefore, computer engineers have to design and utilize hardware innovation methods and tools, such as modeling, simulation, synthesis, analysis, and evaluation.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?