Artificial intelligence (AI) has experienced rapid growth in recent years, and one key factor driving this advancement is the ongoing improvement in AI hardware. The world generates vast amounts of data every day, and to make sense of it all, AI systems require faster and more robust hardware. Manufacturers and researchers are continually developing new devices and architectures to support the tremendous processing power AI needs to reach its full potential IBM Research.
To keep up with the ever-growing demands of AI algorithms, various organizations are focusing on designing AI hardware from the device level. These efforts aim to unlock enhancements in essential metrics, such as energy efficiency and computational capabilities. Building AI hardware specifically tailored for AI applications allows for the creation of unique networks and systems, leading to more effective and powerful AI solutions NIST.
Intel, for instance, offers a comprehensive portfolio of AI hardware designed to simplify AI implementation from preprocessing to modeling to deployment. Their AI hardware solutions have proven successful, with approximately 70 percent of the world’s data center inferencing running on Intel® Xeon® Scalable processors Intel. The ongoing improvements in AI hardware unlock even more opportunities for AI applications across various industries and sectors, driving innovation and efficiency.
AI Hardware Evolution
AI Chips
AI chips are specialized processors designed to handle the complex computational tasks required by artificial intelligence systems, especially in tasks like deep neural networks. These chips are optimized to deliver better performance and energy efficiency than traditional CPUs and GPUs. Companies like IBM and NIST are actively working on fabricating and measuring new AI chips with novel circuits and architectures, seeking to enable future generations of AI with enhanced computing methods and speeds.
GPUs
Graphics Processing Units (GPUs) have been an essential component in the evolution of AI hardware. Initially designed for graphics rendering, GPUs were discovered to be highly efficient for parallel computing, which is crucial when processing large amounts of data for deep learning applications. With their ability to handle multiple tasks simultaneously, GPUs became the go-to hardware for many AI researchers and developers.
CPUs
Central Processing Units (CPUs) are the general-purpose processors that power most computing devices. While CPUs have made considerable advancements in processing speeds and efficiency, their traditional architecture may not be the most suitable for AI applications, which require more specialized hardware to handle the massive amounts of data and computationally intensive algorithms. Nonetheless, CPU manufacturers are investing in R&D to improve their processors’ AI capabilities.
AI Accelerators
AI accelerators are specialized hardware components designed to speed up AI-related tasks, such as inference and training of deep neural networks. These accelerators can either be integrated into existing hardware or designed as standalone devices. IBM Research AI Hardware Center focuses on developing digital and analog AI cores that leverage approximate computing principles to deliver significant improvements in AI compute performance efficiency. Analog AI cores, in particular, show potential for up to 100x performance efficiency improvement compared to current hardware solutions.
The evolution of AI hardware is driven by the growing demand for faster, more efficient processing of the vast amounts of data generated by AI systems. As the field progresses, innovations in AI chips, GPUs, CPUs, and accelerators promise to help unlock the full potential of artificial intelligence.
Key Players in AI Hardware
Intel
Intel is a leading company in AI hardware innovations, focused on creating powerful processing units for various AI applications. Their product portfolio includes CPUs, FPGAs, ASICs, and specialized AI chips like the Intel Nervana Neural Network Processor. They also work on memory and storage optimization, improving AI and ML workloads’ performance.
NVIDIA
NVIDIA is a dominant player in AI hardware, renowned for their Graphics Processing Units (GPUs). Their flagship product, the NVIDIA Tesla, is a popular choice for AI and deep learning applications due to its high performance and energy efficiency. NVIDIA also provides AI software development kits such as CUDA and cuDNN, which enable developers to harness the power of their hardware for AI applications.
IBM Research
IBM Research’s AI Hardware Center is committed to advancing state-of-the-art AI hardware, focusing on better performance, energy efficiency, and scalability. IBM has a history of building powerful processors like the POWER9, designed for AI workloads, and is developing new technologies like resistive computing and neuromorphic chips, which mimic the human brain’s functioning.
Google is actively involved in AI hardware development, with their custom-built Tensor Processing Units (TPUs), specifically designed for machine learning tasks. TPUs offer significant improvements in data validation and standardization compared to conventional hardware, providing higher performance and lower energy consumption. Google’s AI hardware efforts complement their extensive AI software ecosystem, including TensorFlow, a popular machine learning framework.
Startups
Numerous startups are making their mark in the AI hardware space, taking innovative approaches to develop unique hardware solutions for AI applications. Companies like Graphcore, Cerebras Systems, and Mythic are designing advanced AI chips and accelerators, enabling faster and more efficient performance for AI workloads. These startups are exploring new technologies and have the potential to disrupt the AI hardware market, driving further innovation and growth.
Performance Improvement Factors
Energy Efficiency
Energy efficiency is a crucial factor in the development of AI hardware. As AI systems become more complex, they require more computing power, which in turn demands greater energy consumption. To address this challenge, companies are focusing on innovative solutions to improve energy efficiency. For example, the IBM Research AI Hardware Center aims to deliver a 1000-fold improvement in AI performance and energy efficiency within ten years.
Precision
Precision plays a significant role in AI hardware’s ability to accurately perform tasks such as speech recognition and object detection. Lower-precision computation can lead to faster processing times and reduced power consumption. By developing hardware that uses lower-precision arithmetic, AI systems can operate more efficiently without compromising accuracy.
Algorithms
Improvements in algorithms can lead to significant performance gains in AI hardware. Well-designed algorithms can make AI systems more accurate, efficient, and cost-effective. As an example, VentureBeat points out that in some cases, the gains from better algorithms outpace those from improved hardware, especially for big data problems. Moreover, Intel’s research shows that AI accelerators can achieve 10-100x performance improvements across various applications, models, and use-cases by utilizing optimizations in software.
Dataset Size
The size of the dataset used for training AI models is another essential factor in AI hardware performance. Larger datasets typically require more computation, energy, and memory resources. However, they often lead to improved AI accuracy in tasks like machine learning, deep learning, and reinforcement learning. Handling extensive datasets can be made more efficient by employing optimized hardware designs and algorithms that minimize the required computational resources.
To summarize, several factors contribute to the improvement of AI hardware performance, including energy efficiency, precision, algorithms, and dataset size. By focusing on these aspects, AI hardware developers can create more advanced and efficient systems for various AI applications.
AI Hardware for Different AI Technologies
Neural Networks
Neural networks, inspired by the structure of the human brain, are a fundamental technology in artificial intelligence. They are composed of layers of interconnected neurons that transmit data through synapses. These networks require specialized hardware solutions that can handle the complexities and parallel nature of processing the data.
One such solution is the use of Graphics Processing Units (GPUs), which can speed up neural network computations by over 100 times compared to CPUs. Tensor Processing Units (TPUs) are another option designed specifically for AI applications.
Deep Learning
Deep learning is a subset of machine learning that uses deep neural networks with multiple layers of neurons for improved accuracy and efficiency. These networks can be quite computationally intensive, requiring powerful hardware to process large amounts of data quickly.
AI hardware advancements like GPUs and TPUs play a pivotal role in optimizing deep learning computations. Some companies are even developing giant AI chips that could potentially reshape the design and deployment of AI algorithms in the future.
Machine Learning
Machine learning encompasses a variety of AI techniques, including neural networks and deep learning. In addition to GPUs and TPUs, Field-Programmable Gate Arrays (FPGAs) are another type of AI hardware that can be used for machine learning applications.
FPGAs have advantages such as:
- Flexibility: They can be reprogrammed on the fly to suit different algorithms.
- Power efficiency: They consume less power than GPUs and CPUs.
- Low latency: They provide faster response times in some applications.
Neuro-Symbolic AI
Neuro-symbolic AI is an emerging field that combines the strengths of neural networks and symbolic reasoning. This approach aims to leverage the flexibility of neural networks and the explainability of symbolic systems, leading to more powerful and robust AI systems.
To support neuro-symbolic AI, researchers are exploring new AI hardware architectures and brain-inspired circuits. These novel devices may help to deliver better computing methods, speeds, and energy efficiency in AI systems, advancing the potential of neuro-symbolic AI.
AI Hardware for Inference and Training
AI hardware has significantly improved over the years, offering more efficient solutions for both training and inference in artificial intelligence applications. This section discusses some key advancements in AI hardware focused on inference processing, AI hardware composers, and training workstations.
Inference Processing
Inference processing is a crucial aspect of AI hardware, as it determines how well a trained model performs real-time predictions. Specialized AI hardware provides more parallel processing capability, estimated to be up to 10 times more computing power in ANN applications compared to traditional semiconductor devices. Moreover, dedicated AI hardware can support 4-5 times more bandwidth than generic chips, enabling faster and more accurate inference for complex AI applications.
One example to select the best hardware for inference can be the performance comparison between AWS instances like CPU-c5.4xlarge, Nvidia Tesla-K80-p2.xlarge, Nvidia Tesla-T4-g4dn.2xlarge, and Nvidia Tesla-V100-p3.2xlarge. Evaluating the throughput performance helps to choose the most suitable hardware to achieve the desired speed for inference.
AI Hardware Composer
Modern AI requires customized and specialized hardware to meet its computational demands. Companies such as Habana, Graphcore, Cerebras, and SambaNova emerged to capitalize on this trend by creating application-specific integrated circuits (ASICs), designed exclusively for AI applications. These chips can greatly enhance the processing power and execution speed of AI models.
By 2025, it is expected that ASICs will account for about 70 percent of the edge inference market and GPUs 20 percent. This shift towards specialized hardware will play a vital role in AI hardware composition and subsequently provide faster and more efficient systems.
Training Workstations
In addition to inference hardware, AI training workstations play a critical role in the development and maintenance of AI models. Techniques like DeepSpeed help improve the training and inference efficiency using current hardware through innovations in optimization and scaling. These advancements allow developers to train larger and more complex AI models on the present generation of hardware.
In conclusion, advancements in AI hardware technologies are enabling more efficient and cost-effective inference and training workstations. Specialized hardware and AI-centric architectures help unleash the full potential of modern artificial intelligence applications.
AI Hardware in Data Centers and Cloud
High-Performance Computing
High-performance computing (HPC) is a crucial component of AI hardware in data centers and the cloud. HPC systems are designed to process massive amounts of data and execute complex algorithms quickly and efficiently. This level of processing power is critical for AI applications, such as machine learning, deep learning, and natural language processing.
AI Acceleration Technologies
To boost the performance of AI applications, various AI acceleration technologies are being developed and integrated into data centers and cloud infrastructure. These technologies include:
- Graphics Processing Units (GPUs): Originally designed for rendering graphics, GPUs are now increasingly being used for AI tasks due to their parallel processing capabilities.
- Field Programmable Gate Arrays (FPGAs): These devices can be reprogrammed to perform specific tasks, making them highly adaptable for AI workloads.
- Application-Specific Integrated Circuits (ASICs): Designed for specific applications, such as AI, ASICs offer high performance and energy efficiency.
- Tensor Processing Units (TPUs): Google’s custom-built AI accelerators, designed specifically for machine learning tasks.
Energy Consumption
One of the challenges in deploying AI hardware in data centers and cloud environments is energy consumption. AI applications often require immense processing power, which can lead to increased energy use. This has led to the development of new cooling technologies and more energy-efficient hardware architectures to ensure data centers and cloud infrastructure can handle the power demands of AI workloads.
Data Centers and Cloud
AI hardware is now playing a significant role in influencing the design and operation of both data centers and cloud infrastructure. As AI applications become more prevalent, data centers and cloud providers are embracing specialized AI hardware to support these workloads. This includes adopting AI acceleration technologies and optimizing their infrastructure to accommodate AI’s high energy demands.
Overall, the developments in AI hardware are shaping the future of data centers and cloud infrastructure, providing them with the necessary resources to accommodate the growing demand for AI applications.
AI Hardware Applications
Image Recognition
Image recognition is a key application of AI hardware. With the advancements in this field, machines can now identify, classify, and differentiate between objects present in an image. High-performance hardware can greatly increase the efficiency and speed of image recognition processes. For example, the ImageNet dataset is commonly used to benchmark the capabilities of AI hardware in terms of image classification and object detection.
Latest AI hardware such as GPUs and TPUs improve the performance of popular deep learning frameworks like TensorFlow and PyTorch. These frameworks have been optimized to utilize the potential of modern hardware, allowing developers to build and train state-of-the-art image recognition models faster and more effectively.
Speech Recognition
Speech recognition is another application where AI hardware plays a significant role. The ability to understand and transcribe spoken language to text has numerous applications, from virtual assistants to transcription services. AI hardware improvements have significantly contributed to the progress of speech recognition systems.
State-of-the-art speech recognition models require immense computational power to train and deploy. High-performance AI hardware enables these models to reach lower word error rates and respond faster to voice commands, making the technology more practical and efficient for real-world applications.
Navigation
Navigation systems often rely on AI hardware to process large amounts of data and provide accurate, real-time directions. Autonomous vehicles, drones, and robots are a few examples that utilize advanced AI hardware to navigate safely and efficiently.
The performance of navigation systems can be greatly enhanced using modern AI hardware. For example, more powerful GPUs can process complex and data-intensive tasks in real-time, which is crucial for applications like autonomous driving. Moreover, specialized processors like TPUs give navigation systems the ability to run deep learning models with lower latency, leading to better responsiveness and decision-making.
In summary, AI hardware improvements have significantly impacted various applications such as image recognition, speech recognition, and navigation. As AI hardware continues to evolve, we can expect more advancements, enhancing the performance and capabilities of artificial intelligence in these areas.
Challenges and Future Prospects
Scaling and Cost Issues
As the demand for AI continues to grow, one of the primary challenges faced by the industry is scaling and lowering the cost of AI hardware. Large companies have significant resources to invest in AI-focused hardware, but smaller organizations and start-ups may struggle to afford the high-end components required for advanced AI applications. In this context, the cost of AI hardware is a limiting factor in the deployment and adoption of AI technologies across industries.
Two primary factors tend to increase the cost of AI hardware:
- Manufacturing process: The need for custom-designed chips and components for specific AI tasks contributes to higher production costs.
- Technological advancements: Rapid improvements in AI technology require frequent hardware upgrades, adding to the overall cost.
Analog AI and Digital Logic
AI chip technology needs to evolve not only in terms of raw computing power and speed but also in new computing architectures that integrate both analog AI and digital logic. Analog AI offers the possibility of increased efficiency, lower energy consumption, and faster processing times compared to traditional digital circuits.
However, integrating analog AI components within predominantly digital environments poses multiple challenges, such as:
- Design complexity: Analog AI circuits can be harder to construct and require more intricate design processes.
- Noise sensitivity: Analog components are more affected by fluctuations in voltage and temperature, which may lead to errors in calculations.
Floating Point Operations
Floating point operations are crucial in AI applications, especially for deep learning and machine learning models that require high-precision calculations. However, optimizing AI chips for floating point operations can be challenging due to:
- Increased power consumption: Floating point calculations typically consume more energy, raising concerns about overall power efficiency.
- Design trade-offs: Balancing the need for high-precision computing with other design factors, such as area, power, and cost, can be challenging.
In order to overcome these challenges and capitalize on the potential of AI hardware, future efforts should focus on:
- Developing more efficient manufacturing processes to reduce the cost of AI-focused hardware.
- Exploring new chip architectures that seamlessly integrate analog AI and digital logic components.
- Investigating ways to optimize floating point operations without compromising power efficiency, cost, or design complexity.
