Artificial Intelligence (AI) chips, Comparison with CPUs vs GPUs, they are specialized processors designed for high-performance machine learning tasks. This article explores how they compare to CPUs and GPUs, detailing their evolution, computational requirements, and specific types like ASICs and FPGAs. Discover the future of AI systems with AI chips driving efficiency in deep learning and data processing.
Artificial Intelligence (AI) Chips: A Comparison with CPUs and GPUs
The rapid advancement of artificial intelligence (AI) and machine learning (ML) algorithms has created a surging need for processors that offer both high performance and low power consumption. Executing ML algorithms in a timely manner requires significant computing power. Capable of performing the fundamental ML operations efficiently and swiftly. Since ML involves intricate mathematical calculations, processors are being specifically engineered to complete. These computations in a single clock cycle, thereby accelerating the model training process.
This paper examines the various processors used for implementing machine learning algorithms. It further explores the rationale for application-specific processors, exemplified by the Tensor Processing Unit (TPU), an AI Accelerator. The paper concludes with a brief comparative analysis of the Central Processing Unit (CPU), the Graphics Processing Unit (GPU), and the AI accelerator.
Keywords: Artificial Intelligence (AI), AI chip, AI accelerator, machine learning, tensor processing unit, neural network.
Introduction
With the increasing volume and diversity of available data. Statistical analysis has become crucial for obtaining inexpensive and readily available in-depth information. Through the use of AI and ML, algorithms can be programmed to process larger, more complex datasets, yielding faster and more accurate outcomes. By identifying specific models to mitigate unknown risks, companies are uncovering lucrative avenues for business growth. These algorithms assist companies in closing the gap between their services and customers by enabling better decisions with reduced human intervention.
Artificial Intelligence can be broadly defined as a branch of computer science focused on developing intelligent machines capable of executing tasks that typically demand human intelligence. The primary goals of AI systems are to build expert systems that can offer advice and to create systems that can mimic human-like behavior. Illustrates various approaches to developing AI systems.
The fields shown in require extensive computation to train a system with large datasets, necessitating high computing power. Consequently, computing power becomes a limiting factor in AI system development. For example, a deep learning algorithm might need to analyze millions of images to “learn” to recognize a cat in a photo. Therefore, chips explicitly designed for AI are being developed to accelerate the growth of AI systems. Providing superior performance for complex computations while consuming minimal power.
The Evolution of AI Acceleration
Following the second “AI winter”, the growing demand and popularity of AI and ML led to the utilization of various processors and microcontrollers to accelerate the development of AI systems and ML models. As deep learning and machine learning workloads gained prominence in the 2010s, specialized hardware units were either created or adapted from existing hardware to speed up these tasks.
In the 1990s, digital signal processors were employed as neural network accelerators, and Field-Programmable Gate Array (FPGA)-based accelerators were explored for training and inference. The 2000s saw the emergence of CPUs with beneficial features for AI development, such as fast memory access, and efficient arithmetic and logical computing, which led to them replacing digital signal processors as neural network accelerators.
GPUs, originally electronic circuits for processing images, video, and animations, found a growing role in machine learning due to the identical mathematical foundations shared by neural networks and object processing. Given their increasing popularity in machine learning and AI, GPUs continue to evolve to enhance deep learning and machine learning operations for both training and inference.
To achieve increased programmability, faster code porting, and support for major deep learning frameworks, reconfigurable devices like FPGAs and chips like Application-Specific Integrated Circuits (ASICs) can be used to create dedicated inference accelerators with short latencies. FPGAs offer the advantage of easily evolving the hardware based on the AI system’s needs. While the performance of GPUs and FPGAs surpasses that of CPUs for machine learning, ASICs, with their more specific design, can achieve a significantly higher efficiency factor. Developing chips dedicated to deep learning or machine learning—AI Chips—will further boost the efficiency of AI system development.
Computational Requirements for AI
Although AI system development is fundamentally similar to traditional computing, it also necessitates advanced computing technologies for:
- Unstructured Data: Datasets for developing AI systems or ML models often contain unstructured data (e.g., images, video, voice). Consequently, models must be trained through sampling and fitting before being used for data processing.
- Parallel Processing: Processing and training the model typically requires a massive amount of computation, largely involving linear algebraic operations like large matrix multiplication. Massively parallel computing hardware is better suited for such operations than traditional universal processors.
- Near-Memory Computation: The vast number of parameters demands enormous storage capacity, high bandwidth, and low memory access latency. Data localization is a key feature and is ideal for data reuse.
What are Artificial intelligence (AI) Chips and How Do They Work?
AI chips, or AI Accelerators, are application-specific processors designed for both training machine learning models and performing inference using these trained models. Compared to GPUs, AI Accelerators are less general-purpose but excel at calculating matrix multiplications. The output error of neural network layers, and propagating the computed error to adjacent layers. Furthermore, AI accelerators reduce the time required to develop an AI system compared to CPUs and GPUs. Most AI accelerators or chips are built using FPGAs or ASICs.
In a market transitioning toward workload-optimized AI systems, companies are driven to adopt the fastest, most flexible, most power-efficient, and lowest-cost hardware technology for their AI or ML tasks, including development, learning, and inference. The diverse AI chipset architectures available reflect the variety of ML, deep learning, natural language processing, and other AI workloads, ranging from storage-intensive training to compute-intensive inference. To support this range of workloads, manufacturers incorporate a wide array of technologies into their product portfolios and even into embedded AI implementations.
Notable examples of artificial intelligence (AI) Chips include the Google Tensor Processing Unit (TPU), Intel Nervana, Qualcomm AI Chip, LG Neural Engine, and AWS Inferentia.
Types of artificial intelligence (AI) Chips
Due to their highly specific operations, Artificial Intelligence chips require a more customized and specific architecture that can perform complex computations and satisfy the computational requirements of AI. Thus, the two most common circuits used to develop AI chips are Field-Programmable Gate Arrays (FPGA) and Application Specific Integrated Circuits (ASIC). Both circuits can be employed to model different types of artificial intelligence (AI) chips. Depending on the application and system specifications.
ASIC as an AI Accelerator
An Application Specific Integrated Circuit (ASIC) is an integrated circuit designed for a highly specific purpose, rather than a range of general-purpose operations. Despite their high cost, ASICs can be tailored to meet the exact requirements of a product. Reducing the need for integrating additional components.
The main benefits of using ASICs are their small size, which minimizes the need for extra components. ASICs consume less power and offer higher performance compared to other circuits. Since a large number of circuits are integrated onto a single chip, they facilitate high-speed applications. They are also extremely efficient within their specific application.
A significant drawback of ASICs is their low programming flexibility, as they are customized. Because the chips are designed from the ground up, their cost per unit is high, and they also have a longer time-to-market margin.
FPGA as an AI Accelerator
All processors are integrated circuits—electronic circuits built on a silicon chip. Typically, the circuit is fixed during the chip’s design. An FPGA, however, is a type of chip that permits the end-user to reconfigure the circuit after its design by programming it as needed. The FPGA creates a logical circuit that can be reconfigured by connecting or disconnecting various parts of the circuit engraved on the silicon chip.
FPGAs contain a collection of programmable circuits, each capable of performing a small computation, and a programmable interconnect that links these circuits together. This array of programmable circuits allows the FPGA to execute a large number of parallel operations.
The advantages of using an FPGA as an AI accelerator include lower power consumption compared to CPUs and GPUs. Furthermore, programming an FPGA is less expensive than designing an ASIC circuit, and the cost of an FPGA itself is lower than the required cost for designing an ASIC.
Table highlights the key differences between an ASIC circuit and an FPGA
FPGA vs ASIC Comparison
| FPGA | ASIC |
| Faster time to market – No layout or additional steps needed. | Requires more design time to complete all manufacturing steps. |
| The chip area is large. | The chip area is small. |
| Slower and consumes more power compared to ASIC. | Achieves higher speed and consumes lower power. |
| Can be reconfigured to fix bugs. | Cannot be reconfigured once the chip is designed. |
| Lower cost for small volumes compared to ASIC. | Suited for higher-volume mass production. |
Tensor Processing Unit (TPU)
The Tensor Processing Unit (TPU) is an application-specific integrated circuit developed by Google for the TensorFlow machine-learning library and for training neural networks.
Neural Network Model
Neural networks are parallel computing devices that attempt to create a computer model of the brain. The main goal is to develop a system that can perform various computational tasks faster than traditional systems. These tasks encompass pattern recognition and classification, approximation, optimization, and data clustering. Illustrates the model of a neural network that can be trained using the tensor-processing unit.
A neural network is composed of an input layer, an output layer, and one or more hidden layers. The input layer consists of inputs (x1, x2, …xm) and randomly chosen weights corresponding to these inputs. During training, the inputs remain constant throughout the network and only need to be read once. However, the weights for the corresponding inputs are updated with every cycle and for every layer, necessitating constant reading and updating.
The inputs and their corresponding weights are multiplied and summed to yield the total sum of products. This result is then normalized using an activation function. The output of the neural network is generated at the output layer based on the normalized result. The summation of products of inputs and weights is analogous to matrix multiplication.
Therefore, AI accelerators designed for training neural networks require high matrix multiplication computing power and the ability to quickly read and update weights stored in a memory buffer, while inputs can be stored in a buffer and read only once. For inference, the activation values are updated for every layer, while the weights remain constant for a batch. Thus, activation values must be stored in a unified buffer, and the weights determined during training can be stored in slower off-chip memory.
Architecture
The TPUv1 was designed to handle a high volume of low-precision computations. It was initially developed solely for neural network inference because its architecture prioritized a large number of low-precision computations over a small number of high-precision computations.
The diagram the floor plan of a TPUv1 die, introduced by Google in May 2016. The TPU is designed to serve as an accelerator for complex computations like matrix multiplication. The TPU connects to a host system, which sends the instructions and data for the required computations to the TPU. The computation results are then returned to the host system via the same interface.
The Host Interface is responsible for communication with the host system. In this setup, the TPU accelerates matrix multiplications. While the host system manages other general-purpose operations needed for model training. The TPU stores three forms of data: weights in DDR3 Memory, activations in the Unified Buffer for quick reading and updating, and control instructions in the Control Unit.
The host must access the Unified Buffer rapidly to read the inference output and write new inputs for computation. A significant portion of the chip’s space (53%) is occupied by the unified buffer and the Matrix Multiplication Unit (MXU).
TPU Workflow
The diagram illustrates the high-level chip architecture of the TPU. This section details the flow of data and instructions with reference to the diagram.
At startup, the unified buffer and DDR3 storage are empty. The host machine loads the training neural network model onto the TPU. With the model’s weights placed in the DDR3 memory.
The host system then fills the input values (activations) into the unified buffer. The control unit sends a signal to fetch the weights and store them in the MXU. Before the computation of the next batch, the weights are pre-fetched into the Weight FIFO. Ensuring the next set of weights is ready while the current batch is being computed.
MXU
When the host system triggers the inference engine’s execution, the input values and weights are loaded into the MXU, and the result of the matrix multiplication is sent to the Accumulators. The MXU writes the updated activations back to the Unified Buffer through the Accumulators and the Activation Pipeline. The neural network’s activation function resides in the Activation module. The MXU output is accumulated, and the normalized activation value for the input values is calculated. This updated activation value replaces the old value in the Unified Buffer.
These steps are repeated for all hidden layers in the trained neural network model. The activation values from the final layer are sent back to the host system via the Host Interface.
The control flow, marked in red in the diagram, is managed by the control unit. The control unit receives instructions from the host and ensures they are executed in the correct sequence. It handles operations such as when the MXU should perform matrix multiplication. Which weights to pre-fetch, when weights should be loaded into the Weight FIFO. The operations the activation pipeline needs to perform based on the activation function. Essentially, the control flow directs all operations on the chip. The TPU demonstrates superior performance over the CPU and GPU in executing linear algebra computations.
CPU vs GPU vs AI Chip
This section explores the primary differences between various processors and suggests the most appropriate processor for AI system development based on the required system size.
Comparative Analysis
Figure 5 illustrates the key distinctions between the different processors: CPU, GPU, FPGA, and ASIC. The diagram shows a trade-off: as one moves from general-purpose processors (CPU) toward application-specific processors (FPGA/ASIC), the processor’s flexibility decreases, while its operational efficiency increases.
The CPU is a general-purpose processor that allows users to perform diverse operations, albeit with lower efficiency. Conversely, artificial intelligence (AI) Chips, developed using FPGA/ASIC, are limited to the specific operation they were designed for, reducing flexibility but offering high efficiency for complex machine learning computations.
In addition to flexibility and efficiency, a critical factor is processor performance when developing machine-learning models. As shown in the diagram, the TPU or artificial intelligence (AI) Chip outperforms the CPU and GPU when performing predictions using a trained neural network model.
Another performance metric is the number of operations handled per cycle: a CPU can manage tens of operations, a GPU can handle tens of thousands, while a TPU can handle up to 128,000 operations per cycle.
Table summarizes the major differences between the CPU, GPU, and TPU.
CPU, GPU, and TPU Comparison
| CPU | GPU | TPU |
| Executes a scalar operation per cycle. | Executes a vector operation per cycle. | Executes a tensor (matrix) operation per cycle. |
| Designed to solve computational problems in a general fashion. | Designed to accelerate the rendering of graphics. | Designed to perform a specific task: accelerate deep learning. |
| Used for general-purpose programming. | Used for graphics rendering, machine learning, and general-purpose programming. | Used specifically for training and inference of deep learning models. |
| Provides high flexibility and low efficiency. | Provides low flexibility and high efficiency compared to CPU. | Provides high efficiency and low flexibility. |
Processor Selection
Selecting the ideal processor for developing an AI system is a vital step that requires considering numerous factors. Performance, cost, dataset size, and model size are some of the parameters that must be evaluated. When choosing the most suitable processor for a machine learning or AI system. The below outlines scenarios for selecting the ideal processor for developing systems of different scales.
Summary
As demonstrated in this paper, AI Chips possess enormous potential to revolutionize the development of AI systems and deep learning models. AI Chips offer higher throughput for developing machine-learning models compared to other processors. With the escalating demand for machine learning and deep learning. Chip manufacturers can attract more customers by developing chips that can execute compute-intensive operations in less time and deliver higher efficiency. Therefore, AI chips are projected to be in high demand in the near future. Driven by the increasing complexity of deep learning models.
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