Py65 65816 - Add support for the 65C816 to py65

Overview

Add support for the 65C816 to py65

Py65 (https://github.com/mnaberez/py65) is a great simulator for the 6502. Recently I added support for interrupts (https://github.com/tmr4/py65_int) and a debug window (https://github.com/tmr4/py65_debug_window). After success with these modifications, I decided to try adding support for the 65C816. Luckily, py65 is open-source and enhancing it isn't very difficult.

This repository provides a framework for adding support for the 65C816 to py65. I've included the modules I've developed to simulate and test the 65C816. As noted below, a few modifications are needed to the core py65 modules as well.

Screenshot

Screenshot of py65 running Liara Forth on a simulated 65C816

Contents

I've included the main device module, mpu65c816.py, to add simulation support for the 65C816 to py65. I've also include several modules for testing the 65C816 simulation. These include the main unit test module, test_mpu65c816.py, and support modules, test_mpu65816_Common6502.py and test_mpu65816_Common65c02.py, derived largely from similarly named py65 test modules, to test the 65C816 emulation mode simulation. I've also included a binary file, liara.bin, that I modified from Scot W. Stevenson's Liara Forth (https://github.com/scotws/LiaraForth) to work with py65 simulating the 65C816. Note that I'm a Python newbie and appreciate any feedback to make these better.

  • mpu65c816.py

The 65C816 device.

  • test_mpu65c816.py

The main unit test module for the 65C816.

  • test_mpu65816_Common6502.py

Unit tests for 65C816 emulation mode.

  • test_mpu65816_Common65c02.py

Additional 65C02 based unit tests for 65C816 emulation mode.

  • liara.bin

A modified version of Scot W. Stevenson's Liara Forth (https://github.com/scotws/LiaraForth) for testing. Liara Forth is designed to run on the Western Design Center's W65C265SXB development board (https://www.westerndesigncenter.com/wdc/documentation/W65C265SXB.pdf). I've modified the Liara Forth binary to interface with alternate I/O addresses rather than those used by the development board.

Modifications to core py65 modules

The following modifications are needed for py65 to simulate the 65C816:

  1. monitor.py
  • Add a reference to new 65C816 MPU class from devices.mpu65c816 import MPU as CMOS65C816
  • Add the '65C816': CMOS65C816 pair to the Microprocessors dictionary.

License

The mpu65c816.py, test_mpu65816_Common6502.py and test_mpu65816_Common65c02.py modules contain large portions of code from or derived from py65 which is covered by a BSD 3-Clause License. I've included that license as required.

Running the 65C816 Unit Tests

You can run the unit tests with python -m unittest test_mpu65c816.py. The 65C816 simulation passes the py65 6502- and 65C02-based test (507 in total) in emulation mode. Some of tests were modified to run properly with the new device. I still have to create the tests for native mode operations (not a small task). I expect these to take some time and I expect these will turn up many errors in my code.

Testing the 65C816 Simulation with Liara Forth

It wasn't easy to find a sizable program to test with the new 65C816 simulation. You can run the slightly modified version of Liara Forth with python monitor.py -m 65c816 -l liara.bin -g 5000 -i fff0 -o fff1.

Limitations

  1. The new 65C816 device is largely untested. I plan to update it as I work on supporting hardware and code. Use at your own risk. Some know issues:
  • FIXED: ROL and ROR haven't been updated for a 16 bit accumulator.
  • Extra cycle counts haven't been considered for any new to 65816 opcodes.
  • ADC and SBC in decimal mode are likely invalid in 16 bit.
  • Native mode hasn't been tested outside of bank 0. Assume it will fail for this until it is tested. Currently only 3 banks of memory are modeled, by py65 default, but this can easily be changed.
  • The simulation is meant to emulate the actual W65C816. Modelling so far has been based on the 65816 Programming Manual only. I intend to test at least some code against the W65C265SXB development board.
  • Currently no way to break to the py65 monitor.
  • Register wrapping of Direct page addressing modes need tested.
  1. While Liara Forth runs in py65 with the new 65C816 device, it isn't hard to make it crash. I believe this is due to my code, rather than Liara Forth, even though it is marked as an ALPHA version. Liara Forth runs entirely in bank 0. There is no way to break to the monitor since Liara Forth was designed to run on hardware only. It can only be ended with a control-C.

  2. I've successfully run a non-interrupt version of my own 6502 Forth in the new 65C816 device in emulation mode. This isn't surprising since much of the code comes from py65 6502 and 65C02 devices. I expect an interrupt version of it will run as well, but I haven't tested this. I expect that many 6502 programs will run in emulation mode. Note however, that there are differences between the 65C816 operating in emulation mode and the 6502/65C02 that could cause problems with your program.

Status

  • Initial commit: January 11, 2022
  • Successfully tested my 65C02 Forth in emulation mode
  • Was able to run Liara Forth in native mode in block 0.
    • FIXED: (Many words cause it to crash (likely due to one of the limitations listed above).)
    • Currently all numbers print out as 0. Unclear why.

Next Steps

  • Resolve simulator issues with running Liara Forth. I view this as a robust test of the 65816 simulator, other than bank switching, which Liara Forth doesn't handle out of the box. Some hardware specific Liara Forth features will not work with the simulator (KEY? for example which is hardwired to a W65C265SXB development board specific address indicating whether a key has been pressed).
  • Add native mode unit tests.
Official implementation of Meta-StyleSpeech and StyleSpeech

Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation Dongchan Min, Dong Bok Lee, Eunho Yang, and Sung Ju Hwang This is an official code

min95 169 Jan 05, 2023
Searching keywords in PDF file folders

keyword_searching Steps to use this Python scripts: (1)Paste this script into the file folder containing the PDF files you need to search from; (2)Thi

1 Nov 08, 2021
Unsupervised text tokenizer focused on computational efficiency

YouTokenToMe YouTokenToMe is an unsupervised text tokenizer focused on computational efficiency. It currently implements fast Byte Pair Encoding (BPE)

VK.com 847 Dec 19, 2022
Compute distance between sequences. 30+ algorithms, pure python implementation, common interface, optional external libs usage.

TextDistance TextDistance -- python library for comparing distance between two or more sequences by many algorithms. Features: 30+ algorithms Pure pyt

Life4 3k Jan 06, 2023
Finally decent dictionaries based on Wiktionary for your beloved eBook reader.

eBook Reader Dictionaries Finally, decent dictionaries based on Wiktionary for your beloved eBook reader. Dictionaries Catalan 🚧 Ελληνικά (help welco

Mickaël Schoentgen 163 Dec 31, 2022
An ultra fast tiny model for lane detection, using onnx_parser, TensorRTAPI, torch2trt to accelerate. our model support for int8, dynamic input and profiling. (Nvidia-Alibaba-TensoRT-hackathon2021)

Ultra_Fast_Lane_Detection_TensorRT An ultra fast tiny model for lane detection, using onnx_parser, TensorRTAPI to accelerate. our model support for in

steven.yan 121 Dec 27, 2022
simpleT5 is built on top of PyTorch-lightning⚡️ and Transformers🤗 that lets you quickly train your T5 models.

Quickly train T5 models in just 3 lines of code + ONNX support simpleT5 is built on top of PyTorch-lightning ⚡️ and Transformers 🤗 that lets you quic

Shivanand Roy 220 Dec 30, 2022
A raytrace framework using taichi language

ti-raytrace The code use Taichi programming language Current implement acceleration lvbh disney brdf How to run First config your anaconda workspace,

蕉太狼 73 Dec 11, 2022
Mastering Transformers, published by Packt

Mastering Transformers This is the code repository for Mastering Transformers, published by Packt. Build state-of-the-art models from scratch with adv

Packt 195 Jan 01, 2023
Syntax-aware Multi-spans Generation for Reading Comprehension (TASLP 2022)

SyntaxGen Syntax-aware Multi-spans Generation for Reading Comprehension (TASLP 2022) In this repo, we upload all the scripts for this work. Due to siz

Zhuosheng Zhang 3 Jun 13, 2022
Research code for ECCV 2020 paper "UNITER: UNiversal Image-TExt Representation Learning"

UNITER: UNiversal Image-TExt Representation Learning This is the official repository of UNITER (ECCV 2020). This repository currently supports finetun

Yen-Chun Chen 680 Dec 24, 2022
Creating a chess engine using GPT-3

GPT3Chess Creating a chess engine using GPT-3 Code for my article : https://towardsdatascience.com/gpt-3-play-chess-d123a96096a9 My game (white) vs GP

19 Dec 17, 2022
A look-ahead multi-entity Transformer for modeling coordinated agents.

baller2vec++ This is the repository for the paper: Michael A. Alcorn and Anh Nguyen. baller2vec++: A Look-Ahead Multi-Entity Transformer For Modeling

Michael A. Alcorn 30 Dec 16, 2022
open-information-extraction-system, build open-knowledge-graph(SPO, subject-predicate-object) by pyltp(version==3.4.0)

中文开放信息抽取系统, open-information-extraction-system, build open-knowledge-graph(SPO, subject-predicate-object) by pyltp(version==3.4.0)

7 Nov 02, 2022
precise iris segmentation

PI-DECODER Introduction PI-DECODER, a decoder structure designed for Precise Iris Segmentation and Location. The decoder structure is shown below: Ple

8 Aug 08, 2022
BPEmb is a collection of pre-trained subword embeddings in 275 languages, based on Byte-Pair Encoding (BPE) and trained on Wikipedia.

BPEmb is a collection of pre-trained subword embeddings in 275 languages, based on Byte-Pair Encoding (BPE) and trained on Wikipedia. Its intended use is as input for neural models in natural languag

Benjamin Heinzerling 1.1k Jan 03, 2023
SAINT PyTorch implementation

SAINT-pytorch A Simple pyTorch implementation of "Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracing" based on https://arx

Arshad Shaikh 63 Dec 25, 2022
Utilize Korean BERT model in sentence-transformers library

ko-sentence-transformers 이 프로젝트는 KoBERT 모델을 sentence-transformers 에서 보다 쉽게 사용하기 위해 만들어졌습니다. Ko-Sentence-BERT-SKTBERT 프로젝트에서는 KoBERT 모델을 sentence-trans

Junghyun 40 Dec 20, 2022
This is the library for the Unbounded Interleaved-State Recurrent Neural Network (UIS-RNN) algorithm, corresponding to the paper Fully Supervised Speaker Diarization.

UIS-RNN Overview This is the library for the Unbounded Interleaved-State Recurrent Neural Network (UIS-RNN) algorithm. UIS-RNN solves the problem of s

Google 1.4k Dec 28, 2022
Question and answer retrieval in Turkish with BERT

trfaq Google supported this work by providing Google Cloud credit. Thank you Google for supporting the open source! 🎉 What is this? At this repo, I'm

M. Yusuf Sarıgöz 13 Oct 10, 2022