The requirement to support real numbers in computers has existed for as long as computers themselves, yet has always been a more complicated challenge than it at first appears. Why? Because computer-based representations can only represent a finite subset of the continuum of real numbers. Consequently, they can only ever be considered an approximation - thereby demanding a diligent understanding of both the required and achieved precision.
This is where floating-point arithmetic comes in, a technique that now sits at the heart of modern computing and underpins everything from scientific data to machine learning models and 3D graphics. Floating-point provides a standard by which computer users can represent real numbers while permitting computational precision to be traded for processing performance.
The IEEE754 standard has long provided a stable foundation for formatting numbers, but the landscape is shifting rapidly as new hardware architectures, algorithmic innovations, and application demands reshape how floating-point arithmetic is designed and used. Several key trends are driving this evolution.
One of the most significant trends is the widespread adoption of reduced-precision floating-point types. Driven largely by artificial intelligence (AI) and high-performance computing (HPC), formats such as FP16, Bfloat16, FP8, and even 4bit variants like MXFP4 and NVFP4 are becoming mainstream. These formats offer substantial gains in memory bandwidth and computational throughput, enabling faster training and inference in deep learning systems.
AI workloads tolerate lower precision remarkably well due to their statistical nature. Hardware designers like Imagination, NVIDIA, and AMD have responded by integrating specialised AI acceleration units optimised for low-precision operations inside their GPUs. As a result, reduced-precision arithmetic is no longer a niche optimisation - it is becoming a default choice for performance-critical workloads.
Modern processors are increasingly designed with heterogeneous compute units, each tailored for specific numeric workloads. GPUs, TPUs, and domain-specific accelerators now include multiple floating-point pipelines supporting different precisions simultaneously. Imagination’s E-Series GPUs, for example, support integer and floating-point (IEE754 and BFloat) operations, while also providing dedicated hardware acceleration for vector, matrix, and dot product operations too. This architectural diversification reflects a broader trend of floating-point computation no longer being monolithic and reflects industry’s preference for energy efficiency and throughput over strict precision, especially in largescale scientific applications where performance per watt is a critical metric.
The processor architectures that tightly couple dedicated AI pipelines inside their hardware (for minimal data movement and better power efficiency) are the ones that will deliver the efficiency needed by future workloads.
As hardware shifts toward mixed-precision and reduced-precision arithmetic, algorithms must adapt. Numerical analysts are developing new error-compensation techniques, mixed-precision solvers, and adaptive precision strategies that dynamically adjust numeric formats based on stability requirements.
This co-design approach - where algorithms and hardware evolve together - is becoming essential. For example, iterative refinement methods allow low-precision hardware to achieve high-precision results by correcting accumulated errors. Such techniques enable significant speedups without sacrificing accuracy, making them attractive for both AI and traditional scientific computing.
Despite advances, born of the intractability of representing the continuum of real numbers in a finite format, floating-point arithmetic remains inherently imprecise. As more developers encounter numeric issues in 3D graphics, AI, simulation, and data analysis, educational resources and tooling are expanding. Modern programming environments increasingly highlight precision pitfalls, rounding behaviour, and reproducibility concerns.
This trend reflects a broader cultural shift: floating-point literacy is becoming a core skill for engineers, not just specialists.
Looking ahead, the future of floating point computation appears more flexible and heterogeneous. Instead of relying on a single universal format, systems will increasingly mix precisions, adapt dynamically to workload needs, and leverage specialised pipelines to balance accuracy, speed, and energy efficiency.
The evolution of floating-point arithmetic is far from over. As 3D graphics and AI continue to push computational boundaries, floating-point formats and algorithms will keep evolving, ensuring that numeric computation remains a vibrant and innovative field.
Imagination is designing the defining GPU architecture for graphics and AI at the edge. Our latest generation of GPUs, E-Series, provides device manufacturers and application developers with the performance, efficiency and flexibility they need for on-device computing. It contains low power, high density, tightly integrated acceleration for popular lower precision number formats: FP32, FP16, BF16, INT8, FP8, FP4, MXFP. If you’re looking to deploy AI in your next device and want to learn more about the flexibility that an Imagination GPU can bring, get in touch with our team.