About

The Timeloop Front-End (TimeloopFE) is a Python front-end interface to the Timeloop infrastructure, which allows users to model tensor accelerators and explore the vast space of architectures, workloads, and mappings.

TimeloopFE provides a rich Python interface, error checking, and automation tools. With closely-aligned Python and YAML interfaces, TimeloopFE is designed to enable easy design space exploration and automation.

Documentation

Documentation for the full framework is available at timeloop.csail.mit.edu. Documentation for TimeloopFE is available at accelergy-project.github.io/timeloopfe/index.html.

Installation

First, ensure that Timeloop and Accelergy are installed following the Timeloop+Accelergy install instructions.

To install timeloopfe, run the following commands:

git clone
https://github.com/Accelergy-Project/timeloopfe.git 
pip3 install ./timeloopfe

Tutorials and Examples

Tutorials and examples available in the Timeloop and Accelergy exercises repository. In this repository, examples can be found in the workspace/baseline_designs directory and tutorials can be found in the workspace/exercises directory.

Minimal Usage

TimeloopFE interface provides two primary functions: - Input file gathering & error checking - Python interface for design space exploration

import timeloopfe.v4 as tl
from joblib import Parallel, delayed
 
# Basic setup. Gathers input files, checks for errors
spec = tl.Specification.from_yaml_files(
  "your_input_file.yaml", "your_other_input_file.yaml"
)
# Call Timeloop mapper
tl.call_mapper(spec, output_dir="your_output_dir")
# Call Accelergy verbose
tl.call_accelergy_verbose(spec, output_dir="your_output_dir")
 
# Multiprocessed design space exploration
def run_mapper_with_spec(buf_size: int):
  spec = tl.Specification.from_yaml_files(
    "your_input_file.yaml", "your_other_input_file.yaml"
  )
  spec.architecture.find("my_buffer").attributes.depth = buf_size
  return tl.call_mapper(spec, output_dir=f"outputs_bufsize={buf_size}")
 
buf_sizes = [1024, 2048, 4096, 8192, 16384]
results = Parallel(n_jobs=8)(
  delayed(run_mapper_with_spec)(buf_size) for buf_size in buf_sizes
)

Please visit the Timeloop and Accelergy exercises repository for more examples and tutorials.