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TridentVision

Implement yolov26m using a complete FPGA, without any C language code. Requires 160K LUTs and 386 DSP cores (based on Xilinx XC7K325T).

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GITHUB · launched Sep 9, 2026 · 71 upvotes (top 11%) · 1 view

Niche: computer vision software and models · 225 launches, 11 in the last 7 days ▼ -45% · Explore this niche →

Category and tags

  • Horizontal
  • Hardware & robotics
  • Developer
  • AI-native
  • Hobby / open-source project

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Could you build this?

No Implementing a full YOLO computer vision neural network purely in FPGA hardware description languages (Verilog/VHDL) without HLS or C requires specialized digital ASIC/FPGA design expertise.

What it would actually take: Designing this requires writing cycle-accurate RTL in pure Verilog or VHDL to build custom hardware processing units for matrix multiplication, convolution, activation functions, and quantization. The engineer must manage timing closure, block RAM (BRAM) memory access patterns, AXI interfaces, and pipeline parallelism to fit within 160K LUTs and 386 DSPs on a Xilinx Kintex-7. This demands advanced electrical engineering and hardware synthesis expertise that AI coding models cannot autonomously produce.

Competitors

Other products that read as similar to this one : 314 launches clear the similarity bar, closest 8 shown.

Attention rank: #44 of 315 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).

Launched 308 days after the earliest competitor.

The niche, labels, vibe code rating and competitor comparison on this page are analysis by Nicheloom. If you use them, please credit nicheloom.com. How you may use our data.