Balancing Hardware-Software Partitioning in FPGA-Based Systems

When building embedded systems on FPGA platforms, partitioning functionality between hardware and software is rarely straightforward, but always consequential. Get it right, and you can accelerate performance, optimize power, and minimize integration risk. Get it wrong, and you risk falling short of timing targets, overcommitting silicon resources, or undermining flexibility altogether.

This blog explores the core engineering principles behind hardware-software partitioning in FPGA systems, covering the design spectrum, real-world frameworks, and critical architectural considerations. Whether you’re designing for deterministic control, edge AI, or software-defined functionality, partitioning decisions are where system architecture truly begins.

The Strategic Importance of Hardware-Software Partitioning

Partitioning determines what functionality is implemented in hardware (FPGA fabric) versus software (running on embedded processors or microcontrollers). This isn’t just a low-level engineering decision—it’s foundational.

Why it matters:

• Partitioning defines your system’s performance ceiling
• It influences the ease of updates post-deployment
• It directly affects power, cost, and development timelines

For example, implementing all functionality in software may simplify early development, but it risks bottlenecks if workloads exceed CPU capacity. Conversely, an overly hardware-centric implementation may result in long debug cycles and limited flexibility for product updates.

We’ve developed partitioning methodologies over two decades that combine performance modeling, simulation, and architectural foresight. Our teams help customers arrive at the right architecture the first time, whether targeting low-power edge nodes, time-sensitive control systems, or AI-enabled embedded platforms.

Understanding the Hardware-Software Trade-Off Spectrum

Partitioning is about choosing where each system function should live, not where it could live. To do this well, engineers need to evaluate trade-offs across several axes.

Hardware implementation benefits:

Software implementation benefits:

Finding the balance: Start by profiling your system. What are the high-frequency operations? What can tolerate jitter? What may need to change in the field? These questions often reveal clear boundaries between hardware-optimized and software-friendly functions.

A Decision Framework for Engineering Leaders

Partitioning success starts with a clear process. Here’s a proven framework we use at Fidus:

Real-World Case Studies: Innovations by Fidus

Fidus approach:

• Re-partitioned the physical layer pipeline into the programmable logic
• Optimized inter-domain data transfer with AXI4-Stream and tightly-coupled buffers
• Maintained L2 protocol stacks in software for upgrade flexibility

Result:

• 35% reduction in development time
• 2.1× increase in throughput
• No late-stage performance surprises

Fidus approach:

• Moved PID and safety loops into the FPGA fabric
• Encapsulated communication, logging, and tuning parameters in embedded Linux
• Designed the hardware-software boundary to tolerate jitter

Result:

• Improved control loop reliability
• Enabled easy customization via UI
• Met IEC safety timing requirement

Fidus approach:

• Accelerated convolution layers on AMD Versal AI Engines
• Kept pre/post-processing and decision logic in C++ on ARM cores
• Used AMD/Xilinx Vitis AI for toolchain integration

Result:

• <10ms total inference latency
• Field-updatable control logic
• Power consumption reduced by 40%

Before committing to a partitioning strategy, it’s critical to understand what commonly goes wrong. At Fidus, we’ve seen these issues repeatedly derail otherwise solid projects.

Common Pitfalls and How to Avoid Them

Partitioning errors often manifest late, during timing closure, system integration, or worst of all, customer deployment. At Fidus, we see the same patterns repeatedly in remediation projects.

• Relying on trial-and-error instead of modeling: Teams often jump straight into implementation and iterate when performance falls short. This wastes cycles and introduces bias—once RTL is written, inertia kicks in. Start with cycle-accurate profiling and simulation-based analysis.
• Over-partitioning hardware to chase marginal gains: Just because a block can be moved to hardware doesn’t mean it should. Logic overuse leads to routing congestion, longer build times, and harder debugging. If a function doesn’t bottleneck performance or latency, keep it in software.
• Ignoring hardware-software interface planning: We’ve seen systems where the hardware is fast, but DMA setup times kill throughput. Every domain crossing should be modeled. Think in terms of transaction timing, not just bandwidth.
• Failure to simulate across domains: Even well-partitioned designs fail if not co-simulated. For example, we caught a case where a filter block in hardware expected 256 samples per burst, but the software sent 255 due to a rounding bug. It passed unit tests, but broke integration.

Architectural Tactics for Robust Partitioning

Partitioning isn’t just a high-level decision—it gets embedded in every aspect of system architecture. Here’s how we engineer resilient partitioned systems.

• Interface Design and Isolation: Every hardware-software boundary is a contract. Use AXI4-Stream for high-throughput data paths and AXI-Lite for control/status. Implement versioned register maps. Always add sanity bits and signatures to detect bad handshakes.
• Memory Architecture Planning: Plan for DMA alignment, buffer sizes, and contention. Avoid false sharing between cache lines. Choose between BRAM, URAM, and DDR based on access patterns. We often use double-buffering to avoid read/write contention in real-time systems.
• Clock Domain Crossing (CDC) Discipline: If your hardware and software operate in different clock domains (common in Versal or systems with PCIe/PL), use CDC-safe FIFOs or handshakes. Always simulate these crossings with worst-case timing models.
• Synchronization Mechanisms: Use interrupts for event-driven hardware-to-software signals. For polling, define minimum polling periods to avoid saturating the bus. In mixed-criticality systems, assign traffic classes (QoS) to prioritize real-time over background tasks.

Advanced Partitioning Strategies in Complex Systems

In high-complexity platforms, traditional partitioning breaks down. That’s where Fidus leans into advanced methods.

• Dynamic Partial Reconfiguration (DPR) / Dynamic Function eXchange (DFX) with rollback: In one aerospace project, we used DPR/DFX to dynamically load different radar processing pipelines during runtime, without requiring a system reboot. To ensure robustness, we implemented rollback triggers using a watchdog timer and a golden image fallback. This enabled mission mode switching with high reliability, even in safety-critical environments.
• Asynchronous Decoupling for Fail-Safe Behavior: For a medical device, we inserted asynchronous FIFOs between safety logic and UI logic to ensure a fault in the display path couldn’t back-propagate into motor control. This approach turned a single-point failure into a recoverable fault.
• System-Level Co-Design: Rather than partitioning post-facto, we co-designed hardware and software together. Shared UML diagrams, hardware abstraction layers (HAL), and simulation stubs let us converge faster, especially when teams were split geographically.
• Heterogeneous Scheduling: On Versal, we’ve helped customers dynamically assign workloads between AI Engines and the FPGA fabric depending on mode (e.g., low-power vs. high-performance). Partitioning isn’t static anymore—it adapts in real time.

Conclusion: Designing for Evolution

Partitioning decisions today shape the flexibility of your platform tomorrow. Here’s how we help teams’ future-proof at the architectural level:

• Design with modularity in mind: Break large hardware accelerators into composable IP blocks with standard interfaces. We encourage clients to avoid monolithic RTL—use wrappers, parameterization, and interface layering.
• Plan for some iteration while selecting the best partitioning strategy: Even with solid upfront analysis, real-world constraints and system behavior may require refinement. Design architectures that accommodate adjustment as insights emerge.
• Use interface stubs and forward-compatibility fields: In software-driven logic, always plan for unused control fields or status bits in the register map. Fidus often reserves bits for future expansion, even if not yet in the spec.
• Design for silicon migration: We structure logic so that when a customer moves from Zynq to Versal (or from UltraScale+ to AI Edge), their partitioned architecture maps cleanly, with minimal rework in logic or firmware.
• Separate timing and algorithmic constraints: Ensure your system isn’t tightly coupled to a fixed timing model. In one project, this allowed the customer to replace a CNN model with a transformer, without rewriting hardware logic.

What’s at Stake—and Why Engineering Leaders Trust Fidus

Partitioning is the hidden architecture that defines product success. It’s easy to overlook, but hard to fix late. The cost of a misstep? Months of rework, blown silicon budgets, or missed milestones. At Fidus, we’ve spent over 20 years helping engineering leaders build systems that just work—on time, on spec, and ready to evolve.

Why we’re trusted:

Partitioning isn’t a design checkbox. It’s a performance lever, a risk-reduction tool, and a strategic decision. If you’re facing tough calls on acceleration, integration, or scalability, bring us in early.

If your next project requires getting the partitioning right the first time, let’s talk. Fidus helps engineering teams navigate architecture trade-offs with confidence