FPGA & Hardware Acceleration

We work with Xilinx (Vivado) including Artix, Kintex, Virtex, and Zynq series, as well as Intel (Quartus) Cyclone, Arria, and Stratix families. For cost-sensitive applications, we also use Lattice and Microchip FPGAs. Our engineers are certified in both major vendor toolchains.

Yes, we develop custom IP cores for signal processing, communication protocols, motor control, video processing, and more. We also integrate third-party IP cores and can optimize existing designs for better performance or reduced resource utilization.

We provide complete FPGA-to-ASIC migration services including RTL optimization, timing closure, test insertion, and working with foundries for tape-out. This pathway is ideal for high-volume production where per-unit cost reduction justifies the NRE investment.

A typical FPGA development workflow progresses through requirements definition, RTL design in VHDL or Verilog, functional simulation, synthesis, place-and-route, timing analysis, and hardware verification, with a full cycle taking 8 to 20 weeks depending on complexity. The most time-consuming phases are usually functional verification and timing closure for high-speed designs operating above 200 MHz.

FPGA power optimization targets static leakage (reduced by selecting low-power device families), dynamic switching power (reduced by minimizing toggle rates, clock gating unused logic blocks, and using lower clock frequencies), and I/O power (reduced by selecting appropriate I/O standards). Architectural choices like time-multiplexing DSP blocks and using block RAM instead of distributed logic can reduce dynamic power by 30 to 50 percent.

Modern FPGAs integrate hardened multi-gigabit transceivers supporting protocols such as PCIe Gen3/Gen4/Gen5, 10/25/100 Gigabit Ethernet, USB 3.0/3.1, SATA III, JESD204B/C for data converters, and Aurora for custom high-speed links, with per-lane data rates ranging from 1 Gbps to over 58 Gbps. Implementing these interfaces requires careful attention to reference clock jitter, channel loss budgets, and equalization settings.

FPGAs excel at real-time video processing because their parallel architecture can process every pixel simultaneously across pipelined stages, achieving deterministic latency critical for machine vision inspection, medical imaging, and broadcast video at 4K/8K resolutions. Common FPGA video processing pipelines include debayering, color space conversion, noise reduction, scaling, and codec encoding/decoding, all connected through AXI-Stream interfaces.

SoC FPGAs like AMD-Xilinx Zynq and Intel Agilex integrate hardened ARM Cortex-A processors alongside programmable logic fabric, enabling a single-chip solution that runs Linux or an RTOS for control-plane tasks while offloading data-plane processing to custom hardware accelerators. This eliminates the need for a separate processor PCB and provides low-latency communication between software and hardware through AXI interconnects.

FPGA debugging relies on simulation-based verification using testbenches in SystemVerilog or UVM environments, and on-chip debugging using embedded logic analyzers such as Xilinx ILA or Intel SignalTap that capture internal signal activity through the JTAG interface. Formal verification tools can mathematically prove that specific properties hold across all possible input combinations, catching corner-case bugs that simulation might miss.

FPGA prototyping maps an ASIC's RTL design onto large FPGAs to create a functional hardware prototype running at 10 to 100 MHz, enabling real-world software development, driver validation, and system integration months before silicon is available. Multi-FPGA prototyping platforms from providers like Synopsys (HAPS) automatically partition large designs across multiple devices, catching system-level integration bugs before committing to tape-out costs.

Partial reconfiguration allows a defined region of the FPGA fabric to be reprogrammed with new logic while the rest continues operating, enabling dynamic hardware time-multiplexing where different accelerator functions share the same physical resources. This is valuable in software-defined radio systems that switch modulation schemes, data center SmartNICs loading different network processing pipelines, and space-constrained designs where the FPGA cannot hold all required functions simultaneously.