Gaming, augmented reality (AR), and virtual reality (VR) create high demand for GPUs, while ongoing technological innovations such as advances in ray tracing technology and memory bandwidth continue to spur market expansion.
Specialized hardware is now enabling GPUs to accelerate AI and machine learning tasks, creating exciting opportunities for gaming and other applications.
Edge computing
As data from billions of IoT, mobile, and industrial devices continues to accumulate, demand is mounting for high-performance processing and low latency connectivity solutions. Traditional data centers cannot effectively handle such vast quantities of real world information due to bandwidth restrictions and unexpected network disruptions.
GPUs are ideal for AI applications because of their ability to process multiple tasks simultaneously, providing AI applications with an edge when performing real-time inference, such as facial recognition systems or natural language processing.
GPU advances also make HPC as a service more accessible than ever, enabling enterprises without access to capital investments for HPC hardware to leverage this technology more easily than before. Cloud rendering at the edge makes HPC technology available even to enterprises without up-front capital for purchases of equipment.
Breakthroughs in materials science
Researchers are making breakthroughs in materials science that will revolutionize how we work, something which will shape the future of electronics, as progress requires breaking down barriers that hinder progress.
Recent studies demonstrated the power of using a two-step process to effectively discover crystal structures: first generating multiple candidate structures before filtering using cutting-edge graph neural network models based on large datasets; this resulted in discovering over 2.2 million new crystal structures versus just 28,000 previously known.
These emerging trends will likely shape the trajectory of GPU computing over the coming years, providing individuals and organizations with an opportunity for success.
Security
Adopting emerging trends in GPU computing is crucial to making sure that this powerful technology is used at its full potential. From edge computing and artificial intelligence advancements, to developments that promise a future without GPUs. These trends are expected to impact GPU usage for years to come.
Process technology advances have enabled each new chip generation to incorporate more transistors in an ever-smaller area, and since a global signal called a clock synchronizes computation throughout a chip, each increase in transistor density means less clock cycles are required to complete any given task.
But as transistor density has also increased, memory access latencies have also become longer. To accommodate for this trend, designers must adopt solutions which tolerate increased latency by continuing productive work while waiting for data they require.
Energy efficiency
Energy efficiency of GPUs is of great significance, as it enables AI models to run on devices with limited power supplies – thus helping ensure the world advances artificial intelligence sustainably, in ways which benefit humanity and planet alike.
As fabrication technology improves, the number of transistors economically manufactured on a processor die has steadily increased year after year – something known as Moore’s Law has led to unprecedented integration on a single chip. 15 years ago designers could only fit one floating-point arithmetic unit onto their CPU; today hundreds can fit onto just one.
Sending data between locations takes time; to reduce this cost, memory systems are designed with low latency in mind.
AI
Artificial Intelligence (AI) applications demand massive processing power, necessitating dedicated hardware. GPUs were originally created for graphics rendering but now also play an invaluable role in AI thanks to their parallel processing abilities and recent upgrades in CPU core count and memory bandwidth, offering more versatility in handling AI workloads than ever.
TPUs developed by Google are specifically tailored for AI computation and can outstrip GPUs in tasks specific to their architecture – for instance when training deep neural networks due to their enhanced tensor operations capabilities.
Faster AI processing enables real-time responses in critical applications like autonomous driving cars, medical diagnostic tools and social media recommendation systems. Additionally, faster AI processing helps lower cloud costs by offloading some AI work to edge devices.
