AGILAB_AIChip_Lab_Guide

🚀 AGILAB - AI Chip & Hardware Accelerator Project

National Taiwan Normal University (NTNU) | Department of Electrical Engineering

This repository serves as the central hardware implementation hub for AGILAB at National Taiwan Normal University (NTNU). Our research focuses on high-efficiency AI inference engines, optimizing PPA (Power, Performance, Area) for complex edge AI workloads and transformer-based architectures.

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📖 Research Scope & Features

This project implements a scalable AI acceleration framework, integrating specialized hardware IPs for end-to-end inference:

• Computation Core: High-throughput Systolic Array architecture for GEMM and Convolution.
• Non-linear & Normalization: Optimized hardware units for Softmax and LayerNorm to accelerate Transformer inference.
• Quantization: Implementation of DPQU (Dual-Precision Quantization Unit) for adaptive precision data paths.
• Efficiency: Attention-distribution-aware architecture to minimize redundant NPU computations.
• ASIC Ready: All RTL code follows strict synthesis guidelines for future tape-outs.

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💻 Target Platforms

Our designs are verified across a diverse range of Xilinx hardware to ensure scalability:

• Edge SoC: Xilinx PYNQ-Z2 (Zynq-7000)
• High-Performance SoC: Xilinx Zynq UltraScale+ (ZU+) series
• Data Center Accelerator: Xilinx Alveo U55C (HBM2 enabled)

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🛠️ Quick Start (Lab Members Only)

To prevent repository bloat and ensure cross-platform consistency, we use a Tcl-based reconstruction flow.

1. Prerequisites

• Xilinx Vivado (2024.x recommended)
• Git & VS Code (with Verilog/SystemVerilog extensions)

2. Rebuild the Project

Do not open the project folder directly. Use the Vivado Tcl Console to reconstruct the environment:

# 1. Navigate to the repository directory
cd [your_repo_path]

# 2. Initialize the project shell (Settings, Files, & Folders)
source project_config.tcl

# 3. Reconstruct the Block Design (for Zynq/ZU+ systems)
source design_1.tcl

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📜 Development Standards (SOP)

All contributors must follow the internal AGILAB standards:

1. HDL Coding Style: Directional I/O naming (East/South), Synchronous Resets, and 3-Block FSM style.

2. Git Workflow: A source-only workflow. DO NOT commit .runs, .cache, or .xpr files.

3. [PPA Evaluation]: Before the April 22nd evaluation, ensure Timing (WNS > 0) and Power reports are exported.