Pipeline (computing)

Summary

In computing, a pipeline, also known as a data pipeline, is a set of data processing elements connected in series, where the output of one element is the input of the next one. The elements of a pipeline are often executed in parallel or in time-sliced fashion. Some amount of buffer storage is often inserted between elements. Computer-related pipelines include:

Instruction pipelines, such as the classic RISC pipeline, which are used in central processing units (CPUs) and other microprocessors to allow overlapping execution of multiple instructions with the same circuitry. The circuitry is usually divided up into stages and each stage processes a specific part of one instruction at a time, passing the partial results to the next stage. Examples of stages are instruction decode, arithmetic/logic and register fetch. They are related to the technologies of superscalar execution, operand forwarding, speculative execution and out-of-order execution.
Graphics pipelines, found in most g

Official source

https://en.wikipedia.org/wiki/Pipeline_(computing)

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Omnidata: A Scalable Pipeline for Making Multi-Task Mid-Level Vision Datasets from 3D Scans

Ainaz Eftekhar, Amir Roshan Zamir, Alexander Evan Sax

This paper introduces a pipeline to parametrically sample and render static multi-task vision datasets from comprehensive 3D scans from the real-world. In addition to enabling interesting lines of research, we show the tooling and generated data suffice to train robust vision models. Familiar architectures trained on a generated starter dataset reached state-of-the-art performance on multiple common vision tasks and benchmarks, despite having seen no benchmark or non-pipeline data. The depth estimation network outperforms MiDaS and the surface normal estimation network is the first to achieve human-level performance for in-the-wild surface normal estimation-at least according to one metric on the OASIS benchmark. The Dockerized pipeline with CLI, the (mostly python) code, PyTorch dataloaders for the generated data, the generated starter dataset, download scripts and other utilities are all available through our project website.

IEEE2021

Pipeline Synthesis and Optimization from Branched Feedback Dataflow Programs

Endri Bezati, Simone Casale Brunet, Marco Mattavelli, Anatoly Prihozhy

Large dataflow designs are a result of behavioral specification of modern complex digital systems and/or a result of unfolding and transforming looped and branched programs. Since deep-submicron silicon technology provides large amounts of available resources, pipelining optimization without (or with minimal) resource sharing can give significant advantages in performance. High-level synthesis of CAL-programs is particularly popular in computation intensive applications (e.g., image and video processing, cryptography, wireless communication, etc.) where feedback actors with data flows at input and output ports represent loop-like behavior. In this work, we propose techniques for transforming, analysis, speculatively pipelining and optimizing large branched feedback dataflow programs. We develop an accurate algorithm and introduce fast dynamic and mixed static / dynamic heuristics that firstly minimize the number of pipeline stages for a given pipeline-stage time-period, and secondly minimize the overall pipeline registers size by means of appropriate assignment of feedbacks and instructions to pipeline stages. We also propose a genetic algorithm for tuning the heuristics for a particular design. The experimental results show the algorithms we propose give quickly solutions that are very close to accurate solutions and overcomes the earlier developed algorithms regarding computing time and pipeline parameters.

SPRINGER2020

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This paper introduces a new methodology for pipeline synthesis with applications to data flow high-level system design. The pipeline synthesis is applied to dataflow programs whose operators are translated into graphs and dependencies relations that are then processed for the pipeline architecture optimization. For each pipeline-stage time, a minimal number of pipeline stages are first determined and then an optimal assignment of operators to stages is generated with the objective of minimizing the total pipeline register size. The obtained "optimal" pipeline schedule is automatically transformed back into a dataflow program that can be synthesized to efficient hardware implementations. Two new pipeline scheduling: "least cost search branch and bound" and a heuristic technique have been developed. The first algorithm yields global optimum solutions for middle size designs, whereas the second one generates close-to-optimal solutions for large designs. Experimental results on FPGA designs show that the total pipeline register size gain in a range up to 4.68x can be achieved. The new algorithms overcome the known downward and upward direction dataflow graph traversal algorithms concerning the amount of pipeline register size by up to 100% on average.

Ieee-Inst Electrical Electronics Engineers Inc2015