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2026 AI Accelerator Projects · GPU · TPU · FPGA · Edge NPU · TensorRT · OpenVINO

AI Accelerator Projects

Best final-year topics on hardware and software acceleration for AI — CUDA & TensorRT, Edge NPUs (Jetson, Coral), FPGA inference, model quantization & pruning, TVM / OpenVINO compilation, and performance benchmarking for BE, BTech and MTech students.

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40+
Accelerator Topics
6
Core Domains
2026
Industry Aligned
GPU / CUDA / TensorRT Edge NPU / Jetson FPGA Acceleration Quantization · Pruning TVM · OpenVINO · ONNX Benchmarking

AI Accelerator Projects — Hardware & Software Speedups for Neural Networks

AI accelerators specialise in the dense linear algebra that dominates deep learning: matrix multiplies, convolutions and attention. Final-year projects that measure real latency, throughput and energy — on GPU, Edge NPU or FPGA — produce strong, industry-relevant results.

Below are best topics across GPU (CUDA / TensorRT), Edge devices, FPGA, model compression, compiler stacks and systematic benchmarking, with the tools typically used in industry and university labs.

CUDA / cuDNN TensorRT OpenVINO Apache TVM ONNX Runtime Jetson / Coral
# AI Accelerator Project Topic Tools Used
⚡ GPU Acceleration — CUDA · TensorRT · Triton
01GPUTensorRT Optimisation of CNN / YOLO Models for Real-Time InferenceTensorRT, PyTorch/ONNX, Nsight
02GPUCustom CUDA Kernel for Fused Activation + Normalisation OperatorsCUDA C++, cuDNN, PyTorch extension
03GPUTransformer Inference Acceleration with TensorRT and FP16/INT8TensorRT, Hugging Face, ONNX
04GPUMulti-Stream CUDA Pipeline for Concurrent Model ServingCUDA streams, TensorRT, Triton
05GPUMixed-Precision Training Speedup Study (AMP) on Consumer GPUPyTorch AMP, CUDA, Nsight Systems
06GPUBatching and Dynamic Shape Optimisation in TensorRTTensorRT, ONNX, profiling
07GPUGPU Memory Hierarchy Aware Kernel Design for GEMM-like OpsCUDA, shared memory, Nsight Compute
📱 Edge AI Accelerators — Jetson · Coral TPU · Mobile NPU
08EdgeYOLOv8 / MobileNet Deployment on NVIDIA Jetson with TensorRTJetson, TensorRT, DeepStream optional
09EdgeGoogle Coral Edge TPU Pipeline for Image ClassificationCoral, TensorFlow Lite, Edge TPU compiler
10EdgePower–Latency Trade-off Analysis of Edge Inference ModelsJetson tegrastats, power meter, TensorRT
11EdgeOn-Device Speech or Keyword Spotting with Quantized ModelsTFLite / ONNX Runtime, Edge device
12EdgeMulti-Model Edge Pipeline (Detect → Classify → Track)Jetson, TensorRT, OpenCV
13EdgeComparative Study: CPU vs GPU vs NPU on Same Model FamilyJetson, TFLite, benchmarking scripts
🔧 FPGA-Based AI Acceleration
14FPGACNN Accelerator on FPGA using High-Level Synthesis (HLS)Vitis HLS / Intel HLS, FPGA board
15FPGAQuantized Neural Network Inference Engine on Xilinx / Intel FPGAVitis AI / OpenVINO FPGA, DPU
16FPGASystolic Array Design for Matrix Multiply (Simulation or FPGA)HLS / Verilog, simulation tools
17FPGAStreaming Pipeline for Real-Time Image Classification on FPGAVitis, DMA, camera interface concepts
18FPGAResource–Performance Trade-off Study of FPGA vs GPU for Small ModelsFPGA tools, GPU baseline, reports
📉 Model Optimization — Quantization · Pruning · Distillation
19OptPost-Training INT8 Quantization Pipeline with Accuracy RecoveryPyTorch / TensorFlow quant, ONNX
20OptStructured and Unstructured Pruning of CNNs with Fine-TuningPyTorch, sparsity libraries
21OptKnowledge Distillation from Large Teacher to Compact StudentPyTorch, custom loss, eval metrics
22OptMixed-Precision (FP16 / BF16 / INT8) Inference ComparisonTensorRT, ONNX Runtime, PyTorch
23OptWeight Clustering and Huffman-Style Compression StudyPyTorch, compression scripts
24OptNeural Architecture Search (NAS) for Efficient Edge ModelsPyTorch, search space, latency proxy
25OptDynamic / Adaptive Inference (Early Exit, Conditional Computation)PyTorch, custom modules
🛠️ Compilers & Runtimes — TVM · OpenVINO · ONNX Runtime
26CompilerApache TVM End-to-End Compilation for CPU / GPU TargetsApache TVM, AutoTVM / Ansor
27CompilerOpenVINO Model Optimiser and Inference Engine PipelineOpenVINO, Model Optimizer, benchmark_app
28CompilerONNX Conversion and Cross-Runtime Comparison (ORT vs TensorRT)ONNX, ONNX Runtime, TensorRT
29CompilerGraph-Level Optimisations: Operator Fusion and Constant FoldingTVM / ONNX graph tools
30CompilerAuto-Tuning Schedules for a Target Operator on GPUTVM AutoScheduler, CUDA
31CompilerPortable Inference Pipeline: One Model, Multiple BackendsONNX, ORT, OpenVINO, TensorRT
📊 Benchmarking · Profiling · System Design
32BenchEnd-to-End Latency and Throughput Benchmark Suite for Vision ModelsCustom harness, TensorRT / ORT
33BenchRoofline Model Analysis of a CNN Layer on GPUNsight Compute, theoretical peaks
34BenchEnergy Efficiency (Inferences per Joule) Comparison Across DevicesPower measurement, Jetson / GPU
35BenchScalability Study: Batch Size vs Latency / Throughput CurvesTensorRT, logging, plots
36BenchProfiler-Driven Optimisation Case Study (Before / After Speedups)Nsight, TensorRT, report
37BenchServer-Side vs Edge Trade-offs for a Fixed Accuracy TargetCloud GPU + Edge device
🔬 Applied & Research-Oriented Accelerator Topics
38AppliedAccelerated Video Analytics Pipeline (Decode → Infer → Track)DeepStream / FFmpeg, TensorRT
39AppliedTinyML-Style Microcontroller Deployment (Optional Extension)TFLite Micro, MCU concepts
40ResearchOperator Scheduling and Memory Planning for Constrained DevicesTVM, memory planners
41ResearchSparse Matrix Acceleration Concepts on FPGA or GPUCUDA / HLS, sparse formats
42ResearchReproducible Benchmark Protocol for Student AI Accelerator ProjectsScripts, documentation, metrics

Topics align with industry practice (NVIDIA, Intel, edge deployments) and research themes in systems for ML. Contact us for reference material, code or HLS/CUDA kernels, benchmarks, university-format report, PPT and viva Q&A for any topic above.

Why Choose Us for AI Accelerator Projects?

Bangalore-based guidance for BE, BTech and MTech students working on GPU, Edge and FPGA acceleration.

GPU & TensorRT

CUDA kernels, TensorRT engines, mixed precision and multi-stream pipelines with measured latency and throughput.

Edge NPU

Jetson and Coral deployments, power–latency trade-offs and on-device vision or audio pipelines.

FPGA & HLS

HLS-based CNN accelerators, quantized inference engines and resource–performance studies.

Optimization & Compilers

Quantization, pruning, TVM, OpenVINO and ONNX Runtime for portable, efficient inference.

Frequently Asked Questions — AI Accelerator Projects

Top topics include TensorRT optimisation of CNNs and Transformers, custom CUDA kernels, Jetson/Coral edge deployment, FPGA HLS accelerators, INT8 quantization and pruning pipelines, TVM/OpenVINO compilation, and rigorous latency–throughput–energy benchmarking.
NVIDIA CUDA, cuDNN, TensorRT, Triton; Intel OpenVINO; Apache TVM; ONNX Runtime; PyTorch/TensorFlow quantization; Xilinx/Intel FPGA toolchains (Vitis AI, HLS); Jetson and Coral for edge; Nsight and similar profilers.
Yes. Packages include reference material, source code or HLS/CUDA kernels, conversion and benchmark scripts, measured results, university-format report, PPT and viva Q&A.
A general-purpose GPU can run many workloads. An AI accelerator is optimised for neural-network math via specialised cores (Tensor Cores, systolic arrays, NPUs), lower-precision support and tighter power/latency targets for inference or training.