{"product_id":"fpga-neural-accelerator-kit-build-a-cnn-inference-engine","title":"FPGA Neural Accelerator Kit: Build a CNN Inference Engine","description":"\u003ch1\u003eBuild a Custom CNN Inference Engine on Xilinx Artix-7: Beat CPU and GPU Latency with FPGA Acceleration\u003c\/h1\u003e\n\n\u003cp class=\"value-summary\"\u003eEvery part needed, pre-tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 3-5 days.\u003c\/p\u003e\n\n\u003cdiv class=\"specs-strip\"\u003e\n  \u003cspan\u003e\u003cstrong\u003eDifficulty:\u003c\/strong\u003e Advanced\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eBuild Time:\u003c\/strong\u003e 10-12 hrs\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eAge:\u003c\/strong\u003e 18-25\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eSkill:\u003c\/strong\u003e VHDL-Based CNN Acceleration\u003c\/span\u003e\n\u003c\/div\u003e\n\n\u003cp\u003eCode a complete two-layer convolutional neural network inference engine in VHDL, synthesize it onto a Xilinx Artix-7 FPGA, and benchmark its latency head-to-head against CPU and GPU implementations. Designed for B.Tech ECE\/EEE students and research scholars, this project bridges deep learning theory and hardware acceleration - a perfect entry point for accelerator design roles and Smart India Hackathon hardware tracks.\u003c\/p\u003e\n\n\u003ch2\u003eWhat You'll Build\u003c\/h2\u003e\n\u003cp\u003eYou'll build a fully functional FPGA-based neural network accelerator that processes image data with significantly lower latency than a general-purpose processor. The output is not just a working hardware module, but a detailed comparison report showing exactly how much faster an FPGA can execute CNN inference compared to a CPU and GPU - ready for your lab submission, conference paper, or capstone project.\u003c\/p\u003e\n\n\u003ch2\u003eWhat You'll Learn\u003c\/h2\u003e\n\u003cul\u003e\n  \u003cli\u003eWrite and simulate VHDL modules for convolution, pooling, and activation layers\u003c\/li\u003e\n  \u003cli\u003eSynthesize and deploy a neural network on Xilinx Artix-7 FPGA, managing timing and resource constraints\u003c\/li\u003e\n  \u003cli\u003eUse an 8-channel logic analyzer to probe and validate on-chip signals during inference\u003c\/li\u003e\n  \u003cli\u003eBenchmark and compare FPGA, CPU (PyTorch\/TensorFlow), and GPU (CUDA) latency with real data\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch2\u003eKit Contents\u003c\/h2\u003e\n\u003ctable\u003e\n  \u003cthead\u003e\u003ctr\u003e\n\u003cth\u003eComponent\u003c\/th\u003e\n\u003cth\u003eQuantity\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n  \u003ctbody\u003e\n    \u003ctr\u003e\n\u003ctd\u003eXilinx Artix-7 FPGA Board\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eLogic Analyser 8CH\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eUSB Cable\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eM-M Wires\u003c\/td\u003e\n\u003ctd\u003e20\u003c\/td\u003e\n\u003c\/tr\u003e\n  \u003c\/tbody\u003e\n\u003c\/table\u003e\n\n\u003ch2\u003eWhy Buy This Kit Instead of Sourcing Parts Separately\u003c\/h2\u003e\n\u003ctable\u003e\n  \u003cthead\u003e\u003ctr\u003e\n\u003cth\u003eFactor\u003c\/th\u003e\n\u003cth\u003eSourcing Separately\u003c\/th\u003e\n\u003cth\u003eCompoden Kit\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n  \u003ctbody\u003e\n    \u003ctr\u003e\n\u003ctd\u003eCompatibility checks\u003c\/td\u003e\n\u003ctd\u003eYou verify every part\u003c\/td\u003e\n\u003ctd\u003ePre-tested as a system\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eBuild support\u003c\/td\u003e\n\u003ctd\u003eForums and scattered tutorials\u003c\/td\u003e\n\u003ctd\u003eAI companion trained on this exact project\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eTime to first working build\u003c\/td\u003e\n\u003ctd\u003eDays of debugging\u003c\/td\u003e\n\u003ctd\u003eHours, with step-by-step guidance\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eShipping coordination\u003c\/td\u003e\n\u003ctd\u003eMultiple sellers, multiple delays\u003c\/td\u003e\n\u003ctd\u003eOne shipment from Bengaluru in 3-5 days\u003c\/td\u003e\n\u003c\/tr\u003e\n  \u003c\/tbody\u003e\n\u003c\/table\u003e\n\n\u003ch2\u003eWho This Kit Is For\u003c\/h2\u003e\n\u003cp\u003eThis kit is built for final-year B.Tech (ECE\/EEE) students, M.Tech VLSI researchers, and participants in hardware tracks of Smart India Hackathon or DRDO innovation contests. It's also ideal for lab classrooms at IITs, NITs, VIT, BITS, and other institutions introducing FPGA-based deep learning.\u003c\/p\u003e\n\n\u003ch2\u003eBuilt and Backed by Compoden\u003c\/h2\u003e\n\u003cp\u003eEvery Compoden kit ships with an AI build companion trained on this exact project - accessible via a QR code on the box, with WhatsApp and email backup. We've spent 10 years building projects for makers, schools, and institutions across India. If a part fails because of a manufacturing defect, replace it free within 7 days.\u003c\/p\u003e\n\n\u003cdetails\u003e\u003csummary\u003eWhat if I get stuck during the build?\u003c\/summary\u003e\u003cp\u003eSimply scan the QR code to open the AI companion, trained on this exact FPGA neural accelerator project. If you need further help, reach out on WhatsApp - our Bengaluru team has assisted over 15,000 builders with FPGA and VHDL debugging.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eIs this kit suitable for a capstone project?\u003c\/summary\u003e\u003cp\u003eAbsolutely. You'll produce a hardware accelerator and a latency comparison report that directly addresses common B.Tech and M.Tech project requirements in edge AI and reconfigurable computing.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eDo I need prior FPGA experience?\u003c\/summary\u003e\u003cp\u003eFamiliarity with VHDL basics is recommended, but the AI companion guides you through every synthesis step - you'll learn the workflow even if you're new to Artix-7.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eCan I run larger CNNs beyond 2 layers?\u003c\/summary\u003e\u003cp\u003eThe Artix-7 FPGA has limited logic; this kit teaches foundational accelerator design. You can layer multiple accelerators or upgrade to bigger devices later, but the principles scale.\u003c\/p\u003e\u003c\/details\u003e\n\n\u003cdiv class=\"kit-description\"\u003e\n  \u003cp\u003eImplement a 2-layer CNN inference engine in VHDL on Xilinx Artix-7 - compare FPGA vs CPU vs GPU latency.\u003c\/p\u003e\n  \u003ch4\u003eWhat's in this kit\u003c\/h4\u003e\n  \u003cul\u003e\n    \u003cli\u003eXilinx Artix-7 FPGA Board\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/mh-sr602-mini-pir-motion-detector-33v-logic-output-compact-design\"\u003eLogic Analyser 8CH\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/esp32-cam-mb-programmer-module-with-micro-usb-ch340g-plug-play\"\u003eUSB Cable\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003eM-M Wires x20\u003c\/li\u003e\n  \u003c\/ul\u003e\n\u003c\/div\u003e\n\n\u003cscript type=\"application\/ld+json\"\u003e\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"FAQPage\",\n  \"mainEntity\": [\n    {\n      \"@type\": \"Question\",\n      \"name\": \"What is included in the Research Lab Kit 25 FPGA Based Neural Accelerator?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"The Research Lab Kit 25 FPGA Based Neural Accelerator includes all components needed: Xilin, Logic Analyser 8CH, USB Cable, M-M Wires and more. 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