The Fast Fourier Transformation (FFT) is the backbone of real-time signal processing. Radar systems depend on rage, Doppler, and angle FFTs for detection and localization, while multi-channel audio pipelines use FFT-based filtering for noise surpression, echo cancellation, and beamforming. Meeting strict timing requirements in these domains has traditionally forced engineers to chose between digital signal processord (DSPs) and field-programmable gate arrays (FPGAs). Each comes with benefits, but with fundamental limitations.
Ubitium offers a new path. It’s Universal RISC-V Processor combines sequential flexibility with spatial parallelism. This article explains how FFTs run on Ubitium, why the architecture matters for radar and audio engineers.
Why it matters
In real-time signal processing, gains in throughput or latency translate directly into system-level performance. A range FFT in a TDM-MIMO radar must complete before the next chirp arrives; a short-time Fourier transform in a voice pipeline must run within a few milliseconds to avoid audible delays. Miss these deadlines and the entire system falters. Improving the raw efficiency of FFT computation therefore has cascading benefits: longer detection range, finer angular resolution, or more natural audio experiences.
Technical Details
The complexity of Fast Fourier Transformation is described as O(N logN) and directly maps to the hardware resources required in a parallel implementation. For an N-point FFT, the number of basic FFTs (radix-2 butterfly) per layer is n/2, and the number of layers equals to log2(N).
A direct implementation scales linearly with both the number of points and the number of FFT layers. However, beyond small FFT sizes (N > 16), this becomes inefficient in terms of hardware utilization, i.e. the number of processing elements (PEs) grows rapidly, even through many idle during parts of the computation.
To increase hardware utilization, horizontal sequentialization divides the FFT into pipeline stages, each corresponding to one or more layers of the algorithm. Horizontal sequentialization trades off latency (more cycles per FFT) for hardware efficiency (fewer PEs).
Vertical sequentilization time-multiplexes the computation within each pipeline stage, i.e. across the butterflies. White horizontal sequentialization reuses entire pipeline stages across layers, vertical sequentialization reuses PEs within the same stage.
Using Ubitium Universal RISC-V Processors, developers can decide on the degree of horizontal and vertical sequentialization. This allows FFT workloads to be adapted to requirements or available resources:
- Maximal throughput mode → direct implementation
- Area-efficient mode → sequentialized execution
The architecture’s flexibility thus enables fine-tuned trade-offs between performance, resource utilization, and latency, all within the same reconfigurable compute fabric.
Comparison against DSPs and FPGAs
When compared to a DSP, the advantage lies in parallelism. A radix-2 FFT that requires O(N logN) sequential steps on a DSP is spread spatially across Ubitium’s array, cutting execution time dramatically. When compared to an FPGA, Ubitium preserves the spatial execution model but adds efficiency at the word level, higher operating frequency, and CPU-like programmability. Engineers therefore avoid the high development overhead of FPGA design while achieving similar or better performance.
Seamless Integration with AI
Signal processing does not exist in isolation. Modern radar and audio systems increasingly combine FFT-based pipelines with AI algorithms for detection, classification, or enhancement. Conventional heterogeneous platforms force data to shuttle between DSPs, CPUs, and GPUs, adding both latency and complexity. Ubitium avoids this bottleneck. The same compute fabric that executes FFT kernels can seamlessly run neural networks and other AI workloads without moving data across processor boundaries. This unified architecture eliminates transfers, reduces end-to-end latency, and simplifies software design, making it practical to fuse deterministic signal processing with adaptive AI in a single device.
The Takeaway
Ubitium provides a new way for engineers who need both high performance and real-time guarantees. It delivers more throughput and lower latency than DSPs by executing FFTs in parallel, and it achieves greater efficiency and flexibility than FPGAs by operating at the word level, clocking faster, and eliminating the need for HDL-based design. At the same time, it is as easy to program as a conventional microprocessor, allowing developers to compile FFT kernels and run them directly. Crucially, the same compute fabric that accelerates FFTs can also execute AI algorithms without moving data between heterogeneous cores. By unifying deterministic signal processing and adaptive AI in a single architecture, Ubitium reduces system complexity, shortens development cycles, and enables next-generation radar and audio systems to meet higher performance targets.