Algorithm hardware acceleration

When software can no longer meet the target, the algorithm needs a hardware implementation. We map it to hardware using fixed-point analysis, loop pipelining and unrolling, memory banking, and dataflow architecture, starting from your existing model so the hardware can be compared against it at every step.

When this service fits

• A reference implementation exists, but software cannot meet the throughput, latency, or power target.
• The algorithm has exploitable parallelism: loops, streams, or independent channels.
• The precision and area trade-off must be understood before committing to hardware.

Typical application domains

• Digital signal processing: filters, FFTs, resamplers, channelisers
• Image and video processing pipelines
• Wireless and wireline baseband processing
• Cryptography and data-integrity engines
• Custom AI/ML inference operators

What the work covers

• Identifying the kernels worth moving to hardware
• Fixed-point analysis to set word lengths against accuracy requirements
• Loop pipelining, unrolling, memory banking, and dataflow architecture
• Interface and data-movement design so the accelerator is not starved at system level

What you receive

• Synthesisable C++ accelerator source and generated RTL
• Bit-accurate model with accuracy results against the reference
• Testbenches that reuse the reference vectors
• Synthesis reports covering throughput, latency, and area

Deliverables are reproducible and kept together, so another engineer can review and rerun the work. The exact list and acceptance evidence are agreed before work starts.

How it starts

Start with the reference model, the performance target it misses today, and the accuracy the system can accept. A short review of that material usually shows whether the gains will come from parallelism, precision, data movement, or all three.

Related reading

• What is High-Level Synthesis?: loop pipelining, unrolling, and bit-accurate types explained.
• HLS feasibility study: measure the candidate block before committing to implementation.
• All Videalt services: compare engagement models and common questions.