4 reasons to choose FPGA acceleration over GPUs for real-time applications

GPUs are often the first choice when it comes to accelerating compute-intensive applications. They offer impressive processing power, particularly for workloads that can be highly parallelized. But when it comes to real-time applications, raw performance is not always enough. Latency, timing consistency, and data handling can matter just as much. This is where FPGAs have some clear advantages.

1. Accelerate parallel processing through hardware pipelining

FPGAs handle parallelism differently from GPUs. Rather than relying mainly on groups of programmable processing cores, they allow developers to build dedicated processing stages directly into the hardware.

This is particularly useful when data needs to be processed continuously, without waiting for one operation to finish before starting the next.

• Multiple operations at once: Different processing stages can run simultaneously in dedicated hardware.
• Continuous data flow: New data can enter a pipeline while previous data is still being processed.
• Less scheduling overhead: Processing stages are connected through hardware logic, reducing the need for software-managed coordination.

2. Reduce data movement and memory latency

Moving data around can be just as costly as processing it. In many real-time systems, memory access and transfers between components become bottlenecks before computing power does.

FPGAs offer more flexibility in how data is stored, accessed, and processed. Developers can build architectures that keep data close to the processing logic and avoid unnecessary transfers.

• Custom data paths: Data can move directly between processing stages instead of passing through unnecessary intermediate steps.
• On-chip memory: FPGA block RAM and other embedded memory resources can be used for fast, application-specific data access.
• Fewer intermediate transfers: Acquisition, processing, and output stages can be integrated into a single hardware design.

3. Accelerate AI inference for real-time applications

As AI becomes increasingly integrated into real-time systems, inference speed, latency, and power consumption are becoming critical considerations. FPGAs offer a flexible hardware architecture that can be tailored to specific AI models and workloads, making them a compelling option for edge AI and embedded applications.

• Optimize AI inference: Implement dedicated hardware architectures to accelerate neural network operations such as convolutions and matrix multiplications.
• Reduce inference latency: Leverage parallel processing and hardware pipelining to execute AI workloads efficiently.
• Enable Edge AI deployment: Run AI models directly on embedded and edge devices, reducing reliance on cloud infrastructure and network connectivity.
• Optimize power consumption: Adapt numerical precision and hardware resources to the specific requirements of each AI workload.

4. Achieve predictable, low-latency processing

In real-time applications, finishing a task quickly is only part of the equation. The system also needs to deliver results within a predictable time frame. This is one area where FPGAs stand out.

Because developers can define the hardware architecture themselves, they have much greater control over how data moves through the processing chain and how long each operation takes.

• Consistent execution time: Dedicated hardware pipelines make processing times easier to predict and control.
• Lower jitter: A carefully designed FPGA architecture can limit timing variations caused by resource contention and scheduling.
• Tighter control over deadlines: Developers can design the processing path around the application's actual timing requirements.