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🔥 New Course · FPGA + AI · 15 Days

FPGA Neural Network
Accelerator from Scratch

Build a production-grade CNN inference accelerator on FPGA — from fixed-point arithmetic to systolic arrays, Vitis AI deployment, and real edge AI systems. No shortcuts.

15
Deep-Dive Days
60K+
Words of Content
100+
Diagrams & Waveforms
Free
No Paywall
Fixed-Point MathMatrix MultiplySystolic ArrayCNN PipelineHLS / Vitis AIEdge Deployment
Why This Course
Why FPGA for Neural Networks?
Every ML engineer knows PyTorch. Almost none know how to actually build the hardware that runs it. This course bridges that gap.

⚡ 10–100× Better Latency

FPGAs offer deterministic sub-millisecond inference latency — critical for autonomous vehicles, robotics, and real-time video analytics.

🔋 10–50× Power Efficiency

Edge AI on an FPGA at 5–25W vs a GPU at 250–400W. For battery-powered devices, there's no competition.

🎛️ Full Hardware Control

Customize precision (INT4, INT8, FP16), dataflow, memory layout — tailored to your exact neural network architecture.

🏭 Production-Ready Skills

Xilinx Vitis AI, Intel OpenVINO FPGA, and custom DPU designs are in high demand at Qualcomm, NVIDIA, Apple, and defense contractors.

15-Day Course
All 15 Days — Day by Day
Each day is 4,000–5,000 words with diagrams, waveforms, equations, real chip examples, and production checklists.
⚡
Day 1
FPGA vs GPU vs CPU for AI Inference
Why FPGAs win on latency and power. Architecture comparison, roofline model applied to inference hardware, real benchmarks: Xilinx Alveo vs A100 GPU.
FPGA ArchitectureRoofline ModelLatencyPower Efficiency
Start Day 1 →
🔢
Day 2
Fixed-Point Arithmetic & Quantization
Why FP32 is wasteful on FPGA. INT8/INT4 Q-format, quantization error analysis, scale factors, overflow handling, and a pipelined 8-bit MAC unit in Verilog.
Fixed-PointINT8QuantizationQ-Format
Start Day 2 →
✖️
Day 3
Matrix Multiply Accelerator
Tiled GEMM engine: 4×4 MAC array, DSP48 packing (2× throughput), output-stationary dataflow, tiling loop controller, and complete Verilog + testbench.
MAC ArrayDSP48 PackingGEMMTiling
Start Day 3 →
🔲
Day 4
Systolic Array Architecture
TPU-style systolic array on FPGA. Weight-stationary dataflow, PE design, skewed activation feeding, timing diagram, and 4×4 Verilog using generate/genvar.
Systolic ArrayWeight-StationaryPE DesignTPU-style
Start Day 4 →
🧠
Day 5
Convolution Engine for CNNs
2D convolution hardware: sliding window logic, line buffers, kernel weight storage, im2col transformation, and a full 3×3 conv engine with BRAM buffering.
Conv2DLine Bufferim2colBRAM
Start Day 5 →
💾
Day 6
Memory Architecture — BRAM vs DDR
On-chip BRAM vs off-chip DDR4. Memory bandwidth bottleneck analysis, ping-pong buffering, double buffering, AXI4 master interface for DDR access.
BRAMDDR4AXI4Ping-Pong
Start Day 6 →
📊
Day 7
Activation Functions in Hardware
ReLU, Leaky ReLU, Sigmoid, Softmax — synthesizable hardware. LUT-based approximations, CORDIC for complex functions, pipelined activation units.
ReLUSigmoidCORDICLUT Approx
Start Day 7 →
🏊
Day 8
Pooling Layers & Normalization
Max pooling, average pooling, global average pooling hardware. Batch normalization folding into conv weights, and fused BN+ReLU+Pool pipeline.
Max PoolingBatch NormBN FoldingFused Layers
Start Day 8 →
🚀
Day 9
Pipelining & Parallelism
Layer-by-layer pipelining, inter-layer FIFOs, throughput vs latency trade-off, initiation interval (II), data vs model parallelism, and pipeline stall analysis.
PipeliningFIFOInitiation IntervalThroughput
Start Day 9 →
🔧
Day 10
HLS with Vitis HLS
Write CNN layers in C++, synthesize to RTL with Vitis HLS. PIPELINE, UNROLL, ARRAY_PARTITION & DATAFLOW pragmas, full conv layer, co-simulation, and report analysis.
Vitis HLSC++ to RTLPragmasDATAFLOW
Start Day 10 →
🎯
Day 11
Vitis AI — DPU Deployment
AMD/Xilinx DPU IP core, vai_q_pytorch quantization, Vitis AI compiler, VART runtime on Zynq/Kria. Deploy a real ResNet-50 on FPGA — no RTL required.
Vitis AIDPUQuantizationResNet
Start Day 11 →
🌿
Day 12
Power Optimization for Edge AI
Dynamic vs static power, clock gating (BUFGCE), partial reconfiguration, DVFS, precision reduction. Stacking techniques to bring 14W down to <5W for edge AI.
Clock GatingDVFSPartial Reconfig5W Budget
Start Day 12 →
🔄
Day 13
Transformer Attention on FPGA
Self-attention in hardware: QKV matmuls on the systolic array, scaled dot-product, hardware softmax, multi-head parallelism. BERT/ViT inference on Alveo.
AttentionTransformerBERTMulti-Head
Start Day 13 →
📈
Day 14
Benchmarking & Profiling
Measure real TOPS, latency (p99), throughput, power, and efficiency. Peak vs effective TOPS, roofline diagnosis, bottleneck fixes, and MLPerf methodology.
TOPSMLPerfRooflineProfiling
Start Day 14 →
🏭
Day 15
Production Edge AI Systems
From bench to field: platforms (Kria/Versal/Alveo), camera pipelines, OTA model updates, reliability & safety, real case studies, and the FPGA AI career path. Capstone.
Kria/VersalOTA UpdatesCase StudiesCareer
Start Day 15 →