Mohamed A. Bamakhrama mohamed

Compiling baremetal C program for Arm Cortex-M0+

Install Arm GNU GCC compiler. Download the tar.xv archive from: https://developer.arm.com/downloads/-/arm-gnu-toolchain-downloads Define the environmet variable $ARM_GCC_ROOT to point to the location where you extract this archive. This guide was tested with arm-gnu-toolchain-11.3.rel1-x86_64-arm-none-eabi
Update your $PATH and $LD_LIBRARY_PATH as follows:
```
setenv PATH $ARM_GCC_ROOT/bin:$PATH
```

setenv LD_LIBRARY_PATH $ARM_GCC_ROOT/lib:$LD_LIBRARY_PATH

Setting up RISC-V toolchain and simulator

This guide explains the steps needed to build the compiler and simulator toolchain for RISC-V ISA. It assumes RV32GC instruction extension.

Tool links:

GNU GCC Compiler: https://github.com/riscv/riscv-gnu-toolchain
LLVM Compiler: https://github.com/llvm/llvm-project.git
Proxy Kernel (to map syscalls from binary to OS): https://github.com/riscv-software-src/riscv-pk

Using the DECA board with LiteX

Follow LiteX installation steps outlined here
Install Intel Quartus Prime (Lite Edition) from here
Build and run the example shipped with LiteX:

$ cd litex-boards/litex_boards/targets

High-Performance Matrix Multiplication

This is a short post that explains how to write a high-performance matrix multiplication program on modern processors. In this tutorial I will use a single core of the Skylake-client CPU with AVX2, but the principles in this post also apply to other processors with different instruction sets (such as AVX512).

Intro

Matrix multiplication is a mathematical operation that defines the product of

	# Run with make SIM=verilator

	TOPLEVEL_LANG = verilog
	VERILOG_SOURCES = $(shell pwd)/skid_buffer.sv
	TOPLEVEL = skid_buffer
	MODULE = skid_buffer_tb
	EXTRA_ARGS += --coverage --trace-fst --trace-structs

	include $(shell cocotb-config --makefiles)/Makefile.sim

	"""
	Tarjan's strongly connected components algorithm + Topological sort
	Taken from: http://www.logarithmic.net/pfh/blog/01208083168
	Public domain. Use it as you will
	"""


	def strongly_connected_components(graph):
	"""
	Tarjan's Algorithm (named for its discoverer, Robert Tarjan) is a graph

	import pyaudio
	import matplotlib.pyplot as plt
	import numpy
	import wave


	FORMAT = pyaudio.paInt16 # We use 16bit format per sample
	CHANNELS = 1
	RATE = 44100
	CHUNK = 1024 # 1024bytes of data red from a buffer

	#!/usr/bin/env bash
	# journal.sh
	# ==========
	#
	# One daily text file to rule them all.
	#
	# Copyright: 2022 Tyler Cipriani <[email protected]
	# License: GPLv3

	set -euo pipefail

	#!/usr/bin/env sh

	set -euv

	# Updated on April 2019 to reflect changes in GN
	# See:
	# https://github.com/ninja-build/ninja
	# https://gn.googlesource.com/gn/

	# We need a recent git