Modern defense radar systems generate raw data at rates that dwarf most commercial computing applications. A single wideband phased array radar with hundreds of elements, each sampled at gigahertz rates with high-resolution ADCs, can produce multiple terabits of data per second. Turning this torrent of bits into actionable target information in real time requires carefully architected high-speed signal processing pipelines. This article examines the hardware architectures, interconnects, and design methodologies that enable this demanding computational task.

The Data Rate Challenge

Consider a representative airborne AESA radar: 1,024 elements, each producing 16-bit I and 16-bit Q samples at 200 MSPS, yielding approximately 6.5 terabits per second of raw data. Even after digital beamforming reduces this to perhaps 64 simultaneous beams, the aggregate data rate remains at hundreds of gigabits per second. The processing pipeline must perform pulse compression, Doppler filtering, CFAR detection, and parameter extraction on this stream with deterministic latency — typically a few milliseconds or less — to close the sensor-to-shooter loop.

FPGA-Based Front-End Processing

Field-programmable gate arrays (FPGAs) are the workhorses of high-speed radar signal processing. Their massively parallel architecture, deterministic timing, and high-speed serial transceivers make them ideally suited for the front-end processing stages where throughput, not algorithmic complexity, dominates. Modern FPGAs from Xilinx (Versal ACAP) and Intel (Agilex) integrate millions of logic cells, thousands of DSP slices, and high-bandwidth memory (HBM) directly on the silicon interposer.

The digital beamforming stage, which combines element-level data into beams through complex weight-and-sum operations, maps naturally to FPGA DSP resources. A typical implementation uses polyphase filter banks followed by FFT-based beamforming, achieving beam agility measured in nanoseconds. Pulse compression via frequency-domain fast convolution similarly exploits the FPGA's inherent parallelism, with FFT cores operating on streaming data at line rate.

The JESD204B/C serial interface standard has become the dominant interconnect between ADCs and FPGAs, supporting lane rates up to 32 Gbps with deterministic latency. Multi-converter synchronization is achieved through SYSREF signals distributed across the system, enabling phase-coherent sampling across hundreds of channels.

GPU Acceleration for Compute-Intensive Stages

While FPGAs excel at high-throughput, low-complexity processing, GPUs dominate when algorithmic complexity increases. Space-time adaptive processing (STAP), synthetic aperture radar (SAR) image formation, and AI-based target classification involve matrix operations that achieve peak efficiency on GPU architectures with thousands of CUDA or ROCm cores. Modern GPUs with HBM2e or HBM3 memory provide terabytes per second of memory bandwidth — essential for the large covariance matrices involved in adaptive processing.

The challenge lies in moving data between FPGAs and GPUs with minimal latency. PCIe Gen 5 (32 GT/s per lane, 16-lane configurations) and emerging CXL (Compute Express Link) interfaces provide high-bandwidth, low-latency paths. Direct GPU-to-FPGA communication via RDMA over NVLink or similar technologies further reduces data movement overhead. In tightly integrated systems, FPGA-based streaming engines preprocess data into GPU-optimized formats, hiding transfer latency behind computation.

Memory Architecture and Data Flow

Memory hierarchy design is critical for achieving real-time performance. The corner-turn operation — transposing a matrix of range samples by pulse number to enable Doppler processing — requires either large on-chip buffers or high-bandwidth external memory. HBM stacks integrated into the FPGA or GPU package provide an elegant solution, offering 300–400 GB/s of bandwidth with dramatically lower power than discrete DDR interfaces. For larger working sets, DDR5 SDRAM provides capacity at lower cost, though with reduced bandwidth.

Data flow optimization involves careful orchestration of buffering, double-buffering, and pipelining to ensure that no processing stage stalls waiting for data. Tools such as High-Level Synthesis (HLS) for FPGAs and CUDA streams for GPUs help designers express these pipelines at a higher level of abstraction, though hand-tuned RTL and custom CUDA kernels still deliver the best performance for the most demanding paths.

Future Directions

The relentless growth in radar bandwidth and channel count continues to push processing requirements. Emerging technologies such as photonic interconnects for chip-to-chip communication, in-memory computing for adaptive beamforming, and neuromorphic processors for pattern recognition promise to reshape the high-speed processing landscape. However, the fundamental principles — match the architecture to the algorithm, minimize data movement, and pipeline aggressively — will remain essential guides for radar signal processing engineers well into the future.