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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).

Details

External ID
1362678258
Source
GITHUB
Company
—
Product
TridentVision
Website domain
github.com
Launched
Sept. 9, 2026
Cohort
—
Upvotes
71
Upvotes percentile
0.8878426851140149
Tags
—
Fetched at
Sept. 13, 2026, 5:56 p.m.
Updated at
Sept. 13, 2026, 5:56 p.m.

Enrichment

Theme
DeepSeek model deployment and inference
Vertical
Horizontal
Function
Hardware & robotics
Audience
Developer
AI stance
AI-native
Project type
Hobby / open-source project
Normalized one-liner
pure fpga implementation for yolov26m object detection
Manually corrected
False

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 — 110 launches clear the similarity bar, closest 8 shown.

Attention rank: #19 of 111 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 308 days after the earliest competitor.

Other launches for this product

Same idea, different domain

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