1. ツール内容

This tool aims to provide a visual and interactive learning platform for college students, beginners and hardware development enthusiasts to help users systematically understand and master the working principles and implementation methods of AI accelerators. The tool integrates the full-process teaching content from neural network model to hardware deployment, supports modular design and step-by-step simulation, and is suitable for course teaching, project demonstration and self-study expansion.

Tool content includes:

Model level: supports importing simple convolutional neural networks (CNNs), such as LeNet, MiniResNet, etc., and provides model structure visualization.

Algorithm optimization: explains common optimization techniques such as quantization, pruning, Winograd convolution, etc.

Hardware implementation: built-in programmable RTL modules (Verilog/Chisel), showing key designs such as computing units, memory access, and pipelines.：11

2. ターゲット

This tool aims to provide a visual and interactive learning platform for college students, beginners and hardware development enthusiasts to help users systematically understand and master the working principles and implementation methods of AI accelerators. The tool integrates the full-process teaching content from neural network model to hardware deployment, supports modular design and step-by-step simulation, and is suitable for course teaching, project demonstration and self-study expansion.

3. 学べること

Digital Circuit Design

Learners will understand the fundamentals of combinational and sequential logic, state machines (FSM), pipelining, and other core concepts essential for AI hardware development.

Hardware Description Languages (HDL)

The tool offers hands-on experience with Verilog and Chisel to describe, simulate, and synthesize hardware designs, emphasizing RTL-level implementation and verification.

Neural Network Architecture and Operations

Focusing on convolutional neural networks (CNNs), the tool explains inference workflows, feature extraction, activation functions, pooling operations, and dataflow within AI models.

Computation Optimization Techniques

Learners will explore methods such as the Winograd algorithm, quantization, computational reuse, and memory access minimization to improve efficiency in hardware implementations

4. 特徴・魅力

This educational tool is carefully designed to guide learners from fundamental principles to advanced implementation techniques in AI hardware acceleration. Its key features include:

Foundation-Oriented Design

The tool starts with the basics of digital logic and AI computation. It introduces essential knowledge step by step, ensuring accessibility even for those without prior hardware experience.

Modular Learning Structure

The tool is structured in modular stages: logic design → neural network fundamentals → Winograd-based convolution → hardware description → accelerator architecture. Each stage builds on the previous, creating a natural learning flow.