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⚡ Vitis HLS 🧠 CNN Accelerators 🎯 YOLO / Detection 🔗 Transformer 📉 RNN / LSTM 🔢 Quantization ✂️ Pruning 📟 PYNQ Deploy
75+ IEEE 2026 VLSI — Vitis HLS Machine Learning FPGA Acceleration — Bangalore Lab

MTech Projects for VLSI — Vitis HLS ML

Exclusive focus on Xilinx Vitis HLS based Machine Learning accelerators on FPGA — CNN (VGG, ResNet, MobileNet), YOLO object detection, Transformer attention, LSTM/GRU, quantization (INT8/fixed-point), pruning-aware design and PYNQ deployment. Explore 75+ IEEE 2026 MTech VLSI project topics with complete base paper, Vitis HLS C++ sources, Vivado/PYNQ projects, resource reports, university-format report, PPT and expert viva support for MTech and PhD scholars at VTU, Anna University, JNTU and autonomous colleges.

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MTech Projects for VLSI — Vitis HLS Machine Learning Acceleration 2026

MTech VLSI projects for 2026 in this page are focused exclusively on Xilinx Vitis HLS based design of Machine Learning accelerators targeting FPGA — covering CNN layer and network accelerators, YOLO-family object detection pipelines, Transformer attention blocks, RNN/LSTM/GRU sequence models, quantization-aware fixed-point designs, pruning-aware sparse compute, and PYNQ-based end-to-end deployment — grounded in recent IEEE, DAC, FPGA, FPL and MLSys publications (2021–2026).

At ProjectsatBangalore, we support end-to-end Vitis HLS ML projects with complete C++ sources and HLS pragmas, Vivado implementation, resource and latency reports, PYNQ notebooks where applicable, university-format reports for VTU, Anna University, JNTU, IEEE base paper, PPT and viva Q&A coaching.

Vitis HLS ML Specialisations We Cover

  • CNN accelerators (conv, pooling, FC layers)
  • VGG / ResNet / MobileNet network pipelines
  • YOLO object detection on FPGA
  • Transformer / attention accelerators
  • LSTM / GRU sequence model HLS designs
  • Fixed-point & INT8 quantization
  • Pruning-aware sparse accelerators
  • Dataflow, PIPELINE, UNROLL, ARRAY_PARTITION
  • Streaming interfaces (AXI4-Stream)
  • PYNQ / Zynq deployment demos
  • Resource–latency trade-off studies
  • Model export from TensorFlow / PyTorch / ONNX
⚡

Vitis HLS

C++ to RTL synthesis with pragmas and dataflow.

🧠

CNN Accel

Conv, pool, FC layers and full network pipelines.

🎯

YOLO

Object detection accelerators for edge vision.

🔗

Transformer

Attention and matrix-multiply HLS blocks.

🔢

Quantization

Fixed-point / INT8 inference engines.

📟

PYNQ

End-to-end deploy and host–FPGA demos.

Tools & Algorithms

Primary tool is Vitis HLS; supporting frameworks for model export, implementation and deployment.

Vitis HLS Vivado PYNQ TensorFlow PyTorch Python OpenCL / C++ HLS Pragmas Quantization ONNX Xilinx FPGA Vitis AI concepts

75+ Best MTech VLSI Project Topics — Vitis HLS Machine Learning 2026

All topics use Vitis HLS as the primary design tool. Algorithms and supporting tools are listed for each project.

# Project Topic Category Tool / Algorithm
🧠  CNN Layer & Network Accelerators (Vitis HLS)
01Convolution Layer Accelerator with Sliding Window and Line Buffer in Vitis HLSCNNVitis HLS · Conv2D · PIPELINE / UNROLL
02Fully Connected (Dense) Layer Accelerator with Matrix–Vector MultiplyFCVitis HLS · GEMV · ARRAY_PARTITION
03Max-Pooling and Average-Pooling Layer HLS Design with Streaming I/OPoolVitis HLS · AXI-Stream · dataflow
04ReLU / Leaky ReLU / Softmax Activation Units in Vitis HLSActivationVitis HLS · fixed-point · LUT
05Batch Normalization Layer Accelerator for CNN InferenceBNVitis HLS · fused scale/shift
06VGG-16 Feature Extraction Pipeline on FPGA using Vitis HLSVGGVitis HLS · multi-layer · dataflow
07ResNet Residual Block Accelerator with Skip Connection SupportResNetVitis HLS · residual · add unit
08MobileNet Depthwise Separable Convolution AcceleratorMobileNetVitis HLS · DW + PW conv · efficiency
09CNN Accelerator for MNIST / CIFAR-10 Classification End-to-EndClassifyVitis HLS · full net · PYNQ
10Im2Col + GEMM based Convolution Accelerator in Vitis HLSIm2ColVitis HLS · GEMM · buffer design
11Winograd-based Fast Convolution Accelerator for 3×3 KernelsWinogradVitis HLS · Winograd F(2×2,3×3)
12Multi-Channel Convolution Engine with Parallel PE ArrayPE ArrayVitis HLS · UNROLL · array partition
13Streaming CNN Accelerator with AXI4-Stream InterfacesStreamingVitis HLS · AXI-Stream · dataflow
14Layer Fusion (Conv + BN + ReLU) Accelerator for Reduced Memory TrafficFusionVitis HLS · fused kernel · bandwidth
15Resource–Latency Trade-off Study of CNN Accelerator using HLS DirectivesTrade-offVitis HLS · PIPELINE levels · reports
🎯  Object Detection — YOLO Family (Vitis HLS)
16YOLOv3 Tiny Object Detection Accelerator Pipeline in Vitis HLSYOLOVitis HLS · Tiny-YOLO · grid decode
17YOLOv5 Backbone Feature Extractor HLS Design for Edge FPGAYOLOv5Vitis HLS · CSP backbone · quant
18Non-Maximum Suppression (NMS) Hardware Unit in Vitis HLSNMSVitis HLS · sorting · IoU compute
19Anchor Box Decode and Confidence Filtering AcceleratorDecodeVitis HLS · post-process · thresholds
20End-to-End YOLO Inference Pipeline on Zynq / PYNQ using Vitis HLSFull YOLOVitis HLS · Vivado · PYNQ notebook
21Multi-Scale Feature Fusion (FPN-style) Block for Detection HeadsFPNVitis HLS · upsample · concat
22Quantized YOLO Accelerator with INT8 Weights and ActivationsINT8 YOLOVitis HLS · fixed-point · calibration
23SSD (Single Shot Detector) Head Accelerator Comparison with YOLOSSDVitis HLS · multi-box · resources
24Real-Time Pedestrian / Vehicle Detection Demo on FPGAEdge DetectVitis HLS · PYNQ · camera pipeline
25Memory-Efficient YOLO Design with On-Chip Weight BufferingMemoryVitis HLS · BRAM · tiling
🔗  Transformer & Attention Accelerators
26Self-Attention (Scaled Dot-Product) Accelerator in Vitis HLSAttentionVitis HLS · QKV · softmax · matmul
27Multi-Head Attention Block HLS Design for Transformer EncoderMHAVitis HLS · parallel heads · concat
28Feed-Forward Network (FFN) Accelerator for Transformer LayersFFNVitis HLS · two linear · GELU/ReLU
29Layer Normalization Unit for Transformer InferenceLayerNormVitis HLS · mean/var · fixed-point
30Matrix Multiply Engine Optimised for Attention Score ComputationMatMulVitis HLS · systolic concepts · PE
31Tiny Transformer / DistilBERT Style Encoder Block on FPGATiny TransVitis HLS · reduced dims · quant
32Softmax Hardware Implementation with LUT ApproximationSoftmaxVitis HLS · exp approx · stability
33Positional Encoding Generation and Addition UnitPosEncVitis HLS · sin/cos · streaming
34Quantized Transformer Attention with INT8 Matrix MultipliesINT8 AttnVitis HLS · quant matmul · scale
35End-to-End Small NLP Classifier using Transformer Blocks on FPGANLP AccelVitis HLS · encoder · classify head
📉  RNN / LSTM / GRU Sequence Models
36LSTM Cell Accelerator with Forget/Input/Output Gates in Vitis HLSLSTMVitis HLS · gates · tanh/sigmoid
37GRU Cell Accelerator for Efficient Sequence ModellingGRUVitis HLS · update/reset · fewer ops
38Bidirectional LSTM Accelerator for Sequence ClassificationBiLSTMVitis HLS · forward/backward · concat
39Time-Series Forecasting Engine using LSTM HLS DesignForecastVitis HLS · multi-step · streaming
40Sigmoid and Tanh Approximation Units for RNN GatesApproxVitis HLS · piecewise / LUT
41Quantized LSTM Inference Engine with Fixed-Point ArithmeticINT LSTMVitis HLS · Q-format · accuracy study
42Multi-Layer Stacked LSTM Accelerator with Intermediate BuffersStackedVitis HLS · layer pipeline · BRAM
43Speech / Keyword Spotting Pipeline using GRU AcceleratorKWSVitis HLS · feature + GRU · PYNQ
44Attention over LSTM Hidden States for Sequence LabellingAttn-LSTMVitis HLS · attention · sequence
45Resource Comparison of LSTM vs GRU Accelerators on Same FPGACompareVitis HLS · reports · throughput
🔢  Quantization, Fixed-Point & Low-Precision Design
46Fixed-Point CNN Inference Engine with Configurable Bit-WidthFixed-PtVitis HLS · ap_fixed · bit study
47INT8 Quantization-Aware Accelerator Design from Floating-Point ModelINT8Vitis HLS · TF/PyTorch quant · scale
48Dynamic Quantization Support for Activation Ranges in HLS KernelDynamicVitis HLS · runtime scale · clamp
49Binary / Ternary Neural Network Inference Accelerator ConceptsBNNVitis HLS · XNOR · popcount
50Mixed-Precision Layer Design (INT8 Conv + FP16 FC) in Vitis HLSMixedVitis HLS · multi-precision · interfaces
51Accuracy vs Resource Trade-off for Different Quantization Bit-WidthsBit-WidthVitis HLS · 4/8/16-bit · reports
52Quantization Calibration Pipeline Export from Python to HLS HeadersCalibratePython · TF · Vitis HLS headers
53Power-Efficient Quantized Accelerator Design ConsiderationsPowerVitis HLS · Vivado power · estimates
✂️  Pruning, Sparsity & Optimisation
54Pruning-Aware Sparse Convolution Accelerator in Vitis HLSSparse ConvVitis HLS · sparse index · skip zeros
55Channel Pruning Compatible CNN Accelerator DesignChannelVitis HLS · reduced channels · load
56Structured Sparsity Support in Matrix–Vector Multiply EngineStructuredVitis HLS · block sparse · PE
57Loop Tiling and Blocking Strategies for Large Feature MapsTilingVitis HLS · tile size · BRAM bound
58Double Buffering and Ping-Pong Memory for Continuous StreamingBufferVitis HLS · dataflow · dual buffer
59ARRAY_PARTITION and ARRAY_RESHAPE Directive Impact StudyDirectivesVitis HLS · partition · bandwidth
60PIPELINE vs UNROLL Trade-offs for Compute-Bound KernelsPipelineVitis HLS · II · resource reports
61Dataflow Region Design for Multi-Stage ML PipelineDataflowVitis HLS · DATAFLOW · streams
62On-Chip vs Off-Chip Weight Storage Strategy for Large ModelsMemoryVitis HLS · DRAM · BRAM hierarchy
📟  System Integration, PYNQ & End-to-End
63PYNQ Overlay Design for CNN Accelerator with Python Host APIPYNQVitis HLS · Vivado · PYNQ notebook
64AXI4-Lite Control and AXI4-Stream Data Path for ML KernelAXIVitis HLS · interfaces · registers
65Multi-Kernel ML System with DMA and Interrupt-Driven HostDMAVitis HLS · Vivado · DMA engine
66End-to-End Image Classification Demo: Camera → FPGA → DisplayVisionVitis HLS · PYNQ · OpenCV host
67Model Export from TensorFlow/PyTorch to HLS C++ Weight HeadersExportPython · ONNX · code gen
68Co-Simulation and C/RTL Verification Workflow for ML KernelsVerifyVitis HLS · cosim · testbench
69Resource Utilisation Dashboard and Performance Benchmarking ScriptBenchmarkVivado reports · Python parse
70Multi-Model Support (Switchable CNN/RNN) on Single FPGA OverlayMulti-ModelVitis HLS · partial reconfig concepts
71Low-Latency Edge Inference Pipeline for Industrial InspectionIndustrialVitis HLS · YOLO/CNN · PYNQ
72Power and Thermal Estimation of Quantized ML AcceleratorPowerVivado power · activity factors
73Comparative Study: CPU vs GPU vs Vitis HLS FPGA for Same ML ModelCompareVitis HLS · host benchmarks
74HLS Design Space Exploration (DSE) Script for Automatic Pragma SearchDSEPython · Vitis HLS · config sweep
75Complete Vitis HLS ML Project: From Model to PYNQ Demo with ReportFull FlowVitis HLS · Vivado · PYNQ · docs

★ All 75 MTech VLSI project topics are based exclusively on Vitis HLS for Machine Learning acceleration. Each project includes the IEEE 2026 base paper, complete Vitis HLS C++ sources with pragmas, Vivado/PYNQ project files, resource and latency reports, university-format report for VTU / Anna University / JNTU, PPT (20–25 slides) and 50+ viva Q&A specific to the project topic.

FAQ — MTech VLSI Vitis HLS ML Projects

Top topics include: CNN Accelerator for Image Classification using Vitis HLS, YOLOv5 Object Detection Pipeline on FPGA, Quantized Neural Network Inference Engine, Transformer Attention Accelerator, LSTM/GRU Sequence Model HLS Design, Pruning-Aware CNN Accelerator, and PYNQ-based End-to-End ML Demo. All include IEEE base paper, Vitis HLS source, Vivado project, report and viva support.
Primary tool: Xilinx Vitis HLS (C++/OpenCL to RTL). Supporting: Vivado for implementation, PYNQ for deployment, TensorFlow / PyTorch / ONNX for model export and quantization. Algorithms: CNN (VGG, ResNet, MobileNet), YOLO, RNN/LSTM/GRU, Transformer attention, quantization (fixed-point, INT8), pruning. HLS directives: PIPELINE, UNROLL, ARRAY_PARTITION, DATAFLOW, AXI-Stream.
Yes. Every project includes: (1) IEEE 2026 base paper; (2) complete Vitis HLS C++ sources with pragmas; (3) Vivado / PYNQ project files; (4) resource, latency and throughput reports; (5) university-format report for VTU, Anna University, JNTU; (6) PPT (20–25 slides); and (7) 50+ viva Q&A covering HLS methodology, algorithms and result interpretation.
Yes. Reports and presentations are customised to your university’s MTech VLSI / Embedded / ECE format — VTU, Anna University, JNTU, RGPV, PES, RV, Manipal, BITS, NIT and autonomous colleges. Chapter structure, abstract, citation style and evaluation checklist are tailored on request.
Typical completion is 12–25 working days. Simple layer accelerators are ready in 10–14 days. Full network pipelines (YOLO, Transformer, multi-layer CNN) take 18–25 days. Express delivery is available. Contact +91 95919 12372 with your submission date.