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As AI capabilities become essential in everything from industrial automation to smart medical devices, the need for real-time processing at the edge is driving a major shift in hardware strategy. Traditional CPUs and GPUs, while powerful, often fail to meet the latency, power, and footprint demands of embedded systems. This is where FPGAs, particularly custom FPGA design solutions, come into play.
In this blog, we explore why FPGAs are gaining traction as a go-to platform for AI acceleration in embedded applications. You’ll learn what sets them apart, how to optimize them for edge AI workloads, and what techniques are helping designers unlock performance that rivals (and often outpaces) off-the-shelf AI accelerators
Embedded AI has evolved rapidly, and so have the expectations placed on edge hardware. In many applications, real-time responsiveness isn’t a luxury—it’s a requirement. Whether identifying pedestrians in autonomous vehicles or processing ultrasound data in medical imaging, decisions must be made in microseconds, not milliseconds.
While data centers continue to power model training and large-scale inference, there’s growing demand to move AI inference closer to where data is generated. The reasons are clear: reduced latency, lower bandwidth usage, and greater system autonomy.
But the edge environment is constrained—thermally, spatially, and energetically. Power budgets may be as low as 5 to 15 watts. In such conditions, the classic pairing of high-performance CPU and GPU becomes unviable.
CPUs offer general-purpose flexibility but often lack the parallelism needed for deep learning tasks. GPUs are well-suited for AI workloads, but their high power draw, memory overhead, and large footprint make them a poor fit for embedded edge systems—particularly those deployed in harsh or mobile environments.
This creates a performance-efficiency gap that many engineering teams are struggling to close.
Field-Programmable Gate Arrays (FPGAs) offer a compelling middle ground. Their ability to execute custom parallel pipelines, handle low-precision arithmetic, and offload deterministic workloads makes them ideal for AI inference at the edge.
Custom FPGA solutions go one step further: they align the architecture directly with the application. Unlike generic AI accelerators, a custom implementation allows for precise control over latency, memory hierarchy, and compute resource allocation.
Pre-packaged AI acceleration hardware like NPUs or TPUs can deliver good performance, but they’re often designed for broad use cases. That generality limits their effectiveness when an application requires ultra-low latency, strict power envelopes, or domain-specific operations (e.g., sonar, radar, or low-light video).
Custom FPGA-based solutions can be architected for the workload, not around it. This creates a path to performance-per-watt levels that off-the-shelf components struggle to match, especially in mission-critical embedded systems.
Think about what embedded AI truly demands: Low latency. Tight power budgets. Predictable real-time behavior. Most off-the-shelf accelerators weren’t built with these constraints in mind. Now, enter the FPGA – a reconfigurable, deterministic, and remarkably efficient device.
But it’s not just the silicon that makes the difference, it’s what you do with it.
Designing AI on an FPGA isn’t about replicating what a GPU does—it’s about rethinking it from the ground up. Every gate, buffer, and BRAM block is an opportunity for control. The challenge is knowing where to spend your silicon—and how to get the most out of it.
Let’s break down how high-performance embedded AI gets built on a custom FPGA foundation.
FPGAs excel at low-precision math. Instead of sticking to 32-bit floating point, designers often opt for 8-bit or even 4-bit fixed-point formats. But precision isn’t the only lever—per-layer scaling, mixed-precision arithmetic, and saturation-aware rounding can further optimize accuracy within a tight hardware budget.
The goal? Keep critical layers sharp while compressing what you can afford to lose. Custom logic makes that possible.
In traditional software, data flows are abstracted away. In FPGA design, dataflow is everything. Optimizing how data moves—when, where, and how often—is often the difference between a design that works and one that flies.
You’ll want to:
Every nanosecond you shave off the pipeline adds up over millions of inferences per second.
Generic AI IP cores are useful—but often leave performance on the table. In custom designs, neural operations like convolution, pooling, and activation are hand-mapped to the fabric using deeply pipelined logic, DSP slices, and shift registers.
This opens the door to model-specific optimizations: Winograd transformations, separable convolutions, and sparsity pruning. If your model does something unique, your hardware should too.
You can’t “bolt on” real-time behavior after the fact. Meeting hard deadlines requires deterministic processing paths, tight loop bounds, and precise memory scheduling. Custom FPGA designs let you model this from the start, often down to the clock cycle.
It’s not just about speed—it’s about guaranteed speed.
In a recent engagement, Fidus was approached by a defense and surveillance technology provider with a critical challenge: Run high-accuracy object detection on four simultaneous 4K video streams under 10 watts total power without sacrificing real-time performance or system flexibility.
Their existing GPU-based prototype achieved baseline accuracy but fell short across every deployment metric. The system consumed 70+ watts, required active cooling, and couldn’t meet consistent frame rates when processing all streams in parallel.
The customer’s operational environment demanded:
Fidus proposed a custom FPGA-based acceleration architecture designed specifically for this workload. No commercial NPU or GPU could satisfy all requirements simultaneously, not without trade-offs in thermal design, determinism, or power.
We implemented a fully customized CNN inference pipeline on a mid-range AMD Versal AI Edge device. Each stage of the network—convolution, activation, pooling, and classification—was mapped to a deterministic, streaming datapath. There were:
Instead, the architecture was built from the fabric up:
| Metric | Result |
| Total Power Consumption | 8.6W at full load |
| Latency per Frame | 38 microseconds (avg), 44 µs (worst case) |
| Throughput | 4 × 4K@30fps object detection |
| Thermal Headroom | Passive cooling, 12°C below thermal limit |
| Accuracy Impact | <1% drop vs. 32-bit float baseline |
The system was delivered with runtime reconfiguration hooks, allowing updated models to be deployed without hardware changes via partial reconfiguration of the logic fabric. All compute, I/O handling, and data preprocessing were integrated on a single device.
Fidus also provided:
This wasn’t just a hardware accelerator—it was a complete, field-deployable AI subsystem.
Purpose-built. Low-power. Real-time. Maintainable.
Delivering high-performance AI on FPGAs is no longer just about fitting models into logic. The real value lies in building scalable, adaptable, and production-ready systems. At Fidus, we bring a deep bench of advanced techniques that allow our clients to evolve their AI workloads over time, without sacrificing performance or burning engineering cycles on every update.
Here’s how we make it happen.
High-Level Synthesis enables rapid development, but it takes engineering precision to extract real performance. Fidus uses HLS not as a shortcut, but as an accelerator for maintainable RTL-equivalent logic.
Our process involves:
The result? Designs that are source-readable in C/C++ yet deliver deterministic throughput and meet aggressive timing constraints in real silicon.
Many edge AI workloads evolve post-deployment. Instead of redeploying hardware, we build systems with DPR blocks—allowing model logic to be swapped on-the-fly while the system continues running.
A few real-world benefits:
We design bitstream partitions and reconfiguration flows from day one, making DPR a first-class design feature, not an afterthought.
On-chip memory is your most precious asset in embedded AI. We architect memory hierarchies with dataflow as the primary constraint, not as a side effect.
That includes:
When every byte and cycle counts, this level of control is the difference between “close enough” and production-grade performance.
We deliver more than a core accelerator—we integrate into real embedded systems, including:
And when something goes wrong? You don’t just get waveforms—you get visibility.
Fidus builds systems to be tested, verified, and sustained over years of deployment. That’s critical for aerospace, defense, medical, and other regulated markets where traceability, test coverage, and supportability matter.
AI models evolve. So do use cases, regulatory environments, and compute requirements. A static architecture is a liability in an industry moving this fast. At Fidus, we don’t just design for today’s inference workloads—we engineer platforms that support tomorrow’s.
Transformer-based architectures are creeping into edge AI. Quantization schemes are shifting. Attention layers are replacing classic CNN structures in some applications. FPGA-based designs, when built right, can adapt.
We structure our solutions around:
The hardware doesn’t have to change every time your model does. That’s the power of programmable logic—when used with foresight.
Many embedded AI systems aren’t islands—they’re nodes in a broader system. Whether your edge device connects to cloud analytics, shares data with a vehicle bus, or receives OTA model updates, Fidus designs with integration in mind.
That includes:
We’ve helped clients deploy FPGA AI systems into ruggedized vehicles, satellite payloads, low-power IoT nodes, and hospital equipment. Each has different constraints, but the need for reliability and adaptability is universal.
As new FPGA families emerge—with AI-optimized DSP slices, hardened NPU blocks, and increased interconnect bandwidth—our toolchains and design strategies are already aligned.
We’re building:
Whether you’re migrating from Zynq UltraScale+ to Versal or planning a leap to edge ML ASICs with FPGA co-processing, we help you design with options in mind, not constraints.
As AI pushes further into the embedded world, the hardware behind it needs to be smarter, faster, and more efficient. Off-the-shelf accelerators often fall short, especially when power, latency, and adaptability are non-negotiable.
That’s where custom FPGA solutions excel. With the right architecture, you can deploy high-performance AI at the edge without compromising on form factor, power, or future flexibility.
At Fidus, we specialize in turning complex AI workloads into efficient, production-ready FPGA systems. Whether you’re building the next generation of autonomous sensing, medical imaging, or industrial intelligence, we’re here to help you push the limits of embedded AI.
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