What is an AI accelerator?

What is an AI accelerator?

An AI accelerator, also known as a neural processing unit, is a class of specialized hardware or computer system designed to accelerate artificial intelligence (AI) and machine learning applications. These applications include artificial neural networks, machine vision, and other data-intensive or sensor-driven tasks. AI accelerators are often designed with a focus on low-precision arithmetic, novel dataflow architectures, or in-memory computing capability. They can provide up to a tenfold increase in efficiency compared to general-purpose designs, thanks to their application-specific integrated circuit (ASIC) design.

AI accelerators are used in a variety of applications, including robotics, Internet of Things (IoT), and gaming. In gaming, for instance, AI accelerators can enhance pathfinding and navigation, NPC dialog generation, NPC behaviors, animation, physics simulations, and content generation.

AI accelerators can be categorized into two types: those for training AI models and those for inference. The goals of training and inference are different, and it makes sense to design separate processors for each type of workload.

There are several types of AI accelerators available in the market, including Graphics Processing Units (GPUs), Field-Programmable Gate Arrays (FPGAs), and Application-Specific Integrated Circuits (ASICs). Each type has its own advantages and disadvantages. For instance, GPUs are typically used for gaming and other graphics-intensive applications, but they can also be used for training neural networks. FPGAs are chips that can be programmed to perform a specific set of tasks and are often used in applications where speed is critical, such as in data centers and supercomputers.

Companies such as Google, Qualcomm, Amazon, Apple, Facebook, AMD, and Samsung are all designing their own AI ASICs. For example, Google's Tensor Processing Unit (TPU) made a splash in 2015 as one of the first specialized architectures for machine learning and AI applications.

How do AI accelerators work? What types of AI accelerators are available?

AI accelerators are specialized hardware designed to efficiently process AI workloads, such as neural networks and machine learning tasks. They are often designed with high-performance parallel computation capabilities, focusing on low-precision arithmetic, novel dataflow architectures, or in-memory computing capability.

AI accelerators can be categorized into two types: those for training AI models and those for inference. Training involves learning the parameters of a model, which is a computationally intensive task. Inference, on the other hand, involves using the trained model to make predictions, which requires less computational power but needs to be done quickly and efficiently.

There are several types of AI accelerators, including Graphics Processing Units (GPUs), Field-Programmable Gate Arrays (FPGAs), and Application-Specific Integrated Circuits (ASICs).

• GPUs, initially designed for graphics processing, are efficient in processing AI-related workloads due to their parallel processing capabilities. They have been adapted to handle AI tasks, with manufacturers like NVIDIA adding specialized hardware ("tensor cores") to further accelerate AI computations.

• FPGAs are programmable chips that can be configured to perform a wide range of digital logic functions. They are often used in applications that require high-performance and low-latency processing, including AI tasks.

• ASICs are custom chips designed for a specific application. In the context of AI, ASICs are designed to optimize the types of calculations prevalent in AI workloads. They can drastically speed up AI training and inference tasks.

AI accelerators are used in a variety of applications, from edge devices like smartphones and IoT devices to data centers for cloud computing. They are also used in autonomous vehicles, robotics systems, and other automated machines.

The design of AI accelerators is continually evolving to meet the increasing demands of AI applications. This includes improving processing speed, scalability, and energy efficiency. For example, some AI accelerators are designed to support specific applications, focusing on target applications and providing high performance for those specific tasks.

In terms of performance measurement, the TOPS (Tera Operations Per Second) metric is often used. However, real-world performance can be significantly lower than the TOPS value due to factors such as idle compute units waiting for data from memory, synchronization overhead between different parts of the accelerator, and control overhead.

AI accelerators can also be implemented in software, such as in Google's TensorFlow platform. TensorFlow is a open-source software library for machine learning that can be used on a variety of hardware platforms.

How do AI accelerators differ from GPUs?

AI accelerators, also known as AI chips or ASICs (Application-Specific Integrated Circuits), are optimized for AI-specific tasks, providing higher performance and energy efficiency for deep learning applications. They are designed to manage the specific operations that are prevalent in AI models, such as matrix multiplications and convolutions. AI accelerators often have fixed-function for matrix multiplication and software-managed cache. They are tailored to accelerate AI computations, making them significantly faster and more power-efficient compared to using general-purpose processors.

On the other hand, GPUs (Graphics Processing Units) offer a broader range of capabilities and are widely used in various industries beyond AI. GPUs started out as specialized processors for computer graphics, but today's GPUs have evolved into programmable processors, also called General Purpose GPUs (GPGPU). They are still specialized parallel processors, but also highly programmable for a narrow range of applications which can be accelerated with parallel processing. GPUs are great at large-scale dot-products done in batch-processing, which is a common operation in AI workloads. However, a normal GPU has extra logic for accelerating video encoding and decoding, for calculating colors, and a bunch of stuff that gamers, engineers, video editors, etc might want, making them general accelerators.

In terms of performance, the choice between an AI accelerator and a GPU can depend on the specific AI workload, performance requirements, power constraints, and available resources. For instance, in a benchmarking test, Habana Gaudi HPUs (a type of AI accelerator) were found to outperform NVIDIA A100 GPUs in terms of cost-effectiveness when training the YOLOv5 model on the COCO dataset.

What are the benefits of using an AI accelerator?

AI accelerators offer several benefits that make them an essential part of modern computing, particularly for tasks related to artificial intelligence and machine learning:

1. Energy Efficiency — AI accelerators are designed to be highly energy efficient. They can be 100-1,000 times more efficient than general-purpose compute machines, which makes them ideal for tasks that require a lot of computational power.

2. Latency and Computational Speed — AI accelerators can significantly reduce the time it takes to train and execute an AI model. They lower the latency of the time it takes to answer a question, making them valuable for real-time applications.

3. Scalability — AI accelerators can easily handle the parallelization of an algorithm along multiple cores, making it possible to achieve a speed enhancement level equal to the number of cores. This scalability is crucial for handling large-scale AI applications.

4. Heterogeneous Architecture — The architecture of AI accelerators allows a system to accommodate multiple types of workloads, making them flexible and adaptable to various tasks.

5. Cost-Effectiveness — AI accelerators can be more cost-effective than general-purpose processors for large-scale AI applications. They can lead to savings in terms of initial hardware expenses, ongoing operational costs, and potential savings engendered by augmented energy efficiency.

6. Co-evolution with Machine Learning Algorithms — The hardware and software of AI accelerators work cohesively as a unit, resulting in higher performance and energy efficiency. This co-evolution of AI accelerators and efficient machine learning algorithms leads to better energy efficiency and seamless use.

7. Performance and Efficiency in Machine Learning — AI accelerators are meticulously optimized for efficient AI workload processing, like neural networks, offering notable advancements in performance, energy efficiency, and cost-effectiveness compared to traditional general-purpose processors like CPUs.

8. Networking for AI accelerators — As model sizes continue to grow, larger computing clusters with many AI accelerators are needed. Companies like NVIDIA offer high-bandwidth inter-GPU interconnect with NVLink and NVSwitch, which are transforming the networking for AI accelerators.

What are the limitations of AI accelerators?

AI accelerators, specialized hardware designed to expedite the computational aspects of machine learning, have several limitations:

1. Power Consumption — AI accelerators, particularly those designed for heavy processing tasks, can consume a large amount of power.

2. Absence of Industry Standards — AI accelerators are not interoperable due to the lack of industry standards. Different types of these coprocessors have varying capabilities, specified use cases, and limitations.

3. Memory Capacity and Communication Latency — GPUs, widely used as hardware accelerators for AI, have limitations in terms of memory capacity and communication latency.

4. Real-World Performance vs. Theoretical Performance — The real-world performance of an AI accelerator can be significantly lower than its theoretical performance due to factors such as idle computer units waiting for data from memory, synchronization overhead between different parts of the accelerator, and control overhead.

5. Design Challenges — Designing and optimizing hardware for AI is not an easy task. It requires balancing multiple factors, such as performance, power, cost, reliability, and scalability.

6. Benchmarking Limitations — The TOPS (Tera Operations Per Second) value, a common metric for AI accelerators, often does not reflect real-world performance. Depending on the accelerator's architecture and workload characteristics, an accelerator might only achieve 5-10% of its theoretical TOPS value.

7. Latency and Computational Speed — While AI accelerators can lower the latency of processing time, different applications require different levels of computational response latency. For example, autonomous navigation demands a computational response latency limit of 20μs.

8. Hardware Design Complexity — The complexity of AI hardware design can make evolution and optimization difficult and costly.

9. In-Memory Computing Challenges — In-memory computing (IMC)-based hardware reduces latency and energy consumption for compute-intensive tasks. However, designing an energy-efficient interconnect is extremely challenging for IMC.

10. Model Precision — Lower-precision models can improve performance and energy efficiency as they require fewer computational resources for processing. However, this might compromise the accuracy of the AI tasks.

These limitations highlight the need for ongoing research and development in the field of AI accelerators to improve their efficiency, interoperability, and real-world performance.