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Category: image processing

Speaking of potential future WebXPRT workloads

Justin Greene
By Justin Greene
on October 31, 2024

In recent blog posts, we’ve discussed several types of potential future WebXPRT workloads—from an auxiliary AI-focused workload to a WebXPRT battery life test—and many of the factors that we would need to consider when developing those workloads. In today’s post, we’re discussing other types of workloads that we may consider for future WebXPRT versions. We’re also inviting you to send us your WebXPRT workload ideas!

Currently, the most promising web technology for future WebXPRT workloads is WebAssembly (Wasm). Wasm is a binary instruction format that works across all modern browsers, provides a sandboxed environment that operates at native speeds, and takes advantage of common hardware specs across platforms. Wasm’s capabilities offer web developers significant flexibility in running complex client applications within the browser.

We first made use of Wasm in WebXPRT 4’s Organize Album and Encrypt Notes workloads, but Wasm has the potential to support many more types of test scenarios. Here are just a few of the use-case categories that Wasm supports:

  • Gaming
  • Image and video editing
  • Video augmentation
  • CAD applications
  • Interactive learning portals
  • Language translation

Those categories and the possibilities they open for additional workloads are exciting! When thinking through possible new workload scenarios, it’s important to remember that workload proposals need to fit within a set of basic guidelines that uphold WebXPRT’s strengths as a benchmark. You can read about those guidelines in more detail in this blog post, but in short, new workloads ideally should

  • be relevant to real-life scenarios
  • have cross-platform support
  • clearly differentiate in their performance between different types of devices
  • produce consistent and easily replicated results

After testing with WebXPRT or reviewing the list of use cases that Wasm supports, have you considered a new workload or test scenario that you would like to see? If so, please let us know! Your ideas could end up playing a role in shaping the next version of WebXPRT!

Justin

Posted in battery life, benchmark, Benchmarking, BenchmarkXPRT, browser performance, Browser-based benchmarks, Collaborative benchmark development, Cross-platform benchmarks, Future of performance evaluation, image processing, Performance benchmarking, WebAssembly, WebXPRT, WebXPRT 4, What makes a good benchmark? | Tagged , , , , , , , , , |

A clearer picture of WebXPRT 4

Justin Greene
By Justin Greene
on October 7, 2021

The WebXPRT 4 development process is far enough along that we’d like to share more about changes we are likely to make and a rough target date for publishing a preview build. While some of the details below will probably change, this post should give readers a good sense of what to expect.

General changes

Some of the non-workload changes in WebXPRT 4 relate to our typical benchmark update process, and a few result directly from feedback we received from the WebXPRT tech press survey.

  • We will update the aesthetics of the WebXPRT UI to make WebXPRT 4 visually distinct from older versions. We do not anticipate significantly changing the flow of the UI.
  • We will update content in some of the workloads to reflect changes in everyday technology. For instance, we will upgrade most of the photos in the photo processing workloads to higher resolutions.
  • In response to a request from tech press survey respondents, we are considering adding a looping function to the automation scripts.
  • We are investigating the possibility of shortening the benchmark by reducing the default number of iterations from seven to five. We will only make this change if we can ensure that five iterations produce consistently low score variance.

Changes to existing workloads

  • Photo Enhancement. This workload applies three effects to two photos each (six photos total). It tests HTML5 Canvas, Canvas 2D, and JavaScript performance. The only change we are considering is adding higher-resolution photos.
  • Organize Album Using AI. This workload currently uses the ConvNetJS neural network library to complete two tasks: (1) organizing five images and (2) classifying the five images in an album. We are planning to replace ConvNetJS with WebAssembly (WASM) for both tasks and are considering upgrading the images to higher resolutions.
  • Stock Option Pricing. This workload calculates and displays graphic views of a stock portfolio using Canvas, SVG, and dygraph.js. The only change we are considering is combining it with the Sales Graphs workload (below).
  • Sales Graphs. This workload provides a web-based application displaying multiple views of sales data. Sales Graphs exercises HTML5 Canvas and SVG performance. The only change we are considering is combining it with the Stock Option Pricing workload (above).
  • Encrypt Notes and OCR Scan. This workload uses ASM.js to sync notes, extract text from a scanned receipt using optical character recognition (OCR), and add the scanned text to a spending report. We are planning to replace ASM.js with WASM for the Notes task and with WASM-based Tesseract for the OCR task.
  • Online Homework. This workload uses regex, arrays, strings, and Web Workers to review DNA and spell-check an essay. We are not planning to change this workload.

Possible new workloads

  • Natural Language Processing (NLP). We are considering the addition of an NLP workload using ONNX Runtime and/or TensorFlowJS. The workload would use Bidirectional Encoder Representations from Transformers (BERT) to answer questions about a given text. Similar use cases are becoming more prevalent in conversational bot systems, domain-specific document search tools, and various other educational applications.
  • Message Scrolling. We are considering developing a new workload that would use an Angular or React.js to scroll through hundreds of messages. We’ll share more about this possible workload as we firm up the details.

The release timeline

We hope to publish a WebXPRT 4 preview build in the second half of November, with a general release before the end of the year. If it looks as though that timeline will change significantly, we’ll provide an update here in the blog as soon as possible.

We’re very grateful for all the input we received during the WebXPRT 4 planning process. If you have any questions about the details we’ve shared above, please feel free to ask!

Justin

Posted in benchmark, Benchmark metrics, Benchmarking, Browser-based benchmarks, Collaborative benchmark development, Cross-platform benchmarks, Future of performance evaluation, HTML5, image classification, image processing, Performance benchmarking, Web-based testing, WebAssembly, WebXPRT, webxprt 4 | Tagged , , , , |

Considering WebAssembly for WebXPRT 4

Justin Greene
By Justin Greene
on March 25, 2021

Earlier this month, we discussed a few of our ideas for possible changes in WebXPRT 4, including new web technologies that may work well in a browser benchmark. Today, we’re going to focus on one of those technologies, WebAssembly, in more detail.

WebAssembly (WASM) is a binary instruction format that works across all modern browsers. WASM provides a sandboxed environment that operates at native speeds and takes advantage of common hardware specs across platforms. WASM’s capabilities offer web developers a great deal of flexibility for running complex client applications in the browser. That level of flexibility may enable workload scenario options for WebXPRT 4 such as gaming, video editing, VR, virtual machines, and image recognition. We’re excited about those possibilities, but it remains to be seen which WASM use cases will meet the criteria we look for when considering new WebXPRT workloads, such as relevancy to real life, consistency and replicability, and the broadest-possible level of cross-browser support.

One WASM workload that we’re investigating is a web-based machine learning workload with TensorFlow for JavaScript (TensorFlow.js). TensorFlow.js offers pre-trained models for a wide variety of tasks, including image classification, object detection, sentence encoding, and natural language processing. TensorFlow.js originally used WebGL technology on the back end, but now it’s possible to run the workload using WASM. We could also use this technology to enhance one of WebXPRT’s existing AI-themed workloads, such as Organize Album using AI or Encrypt Notes and OCR Scan.

We’re can’t yet say that a WASM workload will definitely appear in WebXPRT 4, but the technology is promising. Do you have any experience with WASM, or ideas for WASM workloads? There’s still time for you to help shape the future of WebXPRT 4, so let us know what you think!

Justin

Posted in benchmark, BenchmarkXPRT, Browser-based benchmarks, Collaborative benchmark development, Cross-platform benchmarks, image processing, Machine learning, Performance benchmarking, TensorFlow, Virtual reality, Web-based testing, WebAssembly, WebGL, WebXPRT, WebXPRT 3 | Tagged , , , , , , , , , |

Understanding the basics of AIXPRT precision settings

Justin Greene
By Justin Greene
on September 5, 2019

A few weeks ago, we discussed one of AIXPRT’s key configuration variables, batch size. Today, we’re discussing another key variable: the level of precision. In the context of machine learning (ML) inference, the level of precision refers to the computer number format (FP32, FP16, or INT8) representing the weights (parameters) a network model uses when performing the calculations necessary for inference tasks.

Higher levels of precision for inference tasks help decrease the number of false positives and false negatives, but they can increase the amount of time, memory bandwidth, and computational power necessary to achieve accurate results. Lower levels of precision typically (but not always) enable the model to process inputs more quickly while using less memory and processing power, but they can allow a degree of inaccuracy that is unacceptable for certain real-world applications.

For example, a high level of precision may be appropriate for computer vision applications in the medical field, where the benefits of hyper-accurate object detection and classification far outweigh the benefit of saving a few milliseconds. On the other hand, a low level of precision may work well for vision-based sensors in the security industry, where alert time is critical and monitors simply need to know if an animal or a human triggered a motion-activated camera.

FP32, FP16, and INT8

In AIXPRT, we can instruct the network models to use FP32, FP16, or INT8 levels of precision:

  • FP32 refers to single-precision (32-bit) floating point format, a number format that can represent an enormous range of values with a high degree of mathematical precision. Most CPUs and GPUs handle 32-bit floating point operations very efficiently, and many programs that use neural networks, including AIXPRT, use FP32 precision by default.
  • FP16 refers to half-precision (16-bit) floating point format, a number format that uses half the number of bits as FP32 to represent a model’s parameters. FP16 is a lower level of precision than FP32, but it still provides a great enough numerical range to successfully perform many inference tasks. FP16 often requires less time than FP32, and uses less memory.
  • INT8 refers to the 8-bit integer data type. INT8 data is better suited for certain types of calculations than floating point data, but it has a relatively small numeric range compared to FP16 or FP32. Depending on the model, INT8 precision can significantly improve latency and throughput, but there may be a loss of accuracy. INT8 precision does not always trade accuracy for speed, however. Researchers have shown that a process called quantization (i.e., approximating continuous values with discrete counterparts) can enable some networks, such as ResNet-50, to run INT8 precision without any significant loss of accuracy.

Configuring precision in AIXPRT

The screenshot below shows part of a sample config file, the same sample file we used for our batch size discussion. The value in the “precision” row indicates the precision setting. This test configuration would run tests using INT8. To change the precision, a tester simply replaces that value with “fp32” or “fp16” and saves the changes.

Config_snip

Note that while decreasing the precision from FP32 to FP16 or INT8 often results in larger throughput numbers and faster inference speeds overall, this is not always the case. Many other factors can affect ML performance, including (but not limited to) the complexity of the model, the presence of specific ML optimizations for the hardware under test, and any inherent limitations of the target CPU or GPU.

As with most AI-related topics, the details of model precision are extremely complex, and it’s a hot topic in cutting edge AI research. You don’t have to be an expert, however, to understand how changing the level of precision can affect AIXPRT test results. We hope that today’s discussion helped to make the basics of precision a little clearer. If you have any questions or comments, please feel free to contact us.

Justin

Posted in AI, AIXPRT, Benchmark metrics, Benchmarking, computer vision, image processing, Machine learning, object detection, ResNet-50 | Tagged , , , , , , , , , |

Understanding AIXPRT results

Justin Greene
By Justin Greene
on August 1, 2019

Last week, we discussed the changes we made to the AIXPRT Community Preview 2 (CP2) download page as part of our ongoing effort to make AIXPRT easier to use. This week, we want to discuss the basics of understanding AIXPRT results by talking about the numbers that really matter and how to access and read the actual results files.

To understand AIXPRT results at a high level, it’s important to revisit the core purpose of the benchmark. AIXPRT’s bundled toolkits measure inference latency (the speed of image processing) and throughput (the number of images processed in a given time period) for image recognition (ResNet-50) and object detection (SSD-MobileNet v1) tasks. Testers have the option of adjusting variables such as batch size (the number of input samples to process simultaneously) to try and achieve higher levels of throughput, but higher throughput can come at the expense of increased latency per task. In real-time or near real-time use cases such as performing image recognition on individual photos being captured by a camera, lower latency is important because it improves the user experience. In other cases, such as performing image recognition on a large library of photos, achieving higher throughput might be preferable; designating larger batch sizes or running concurrent instances might allow the overall workload to complete more quickly.

The dynamics of these performance tradeoffs ensure that there is no single good score for all machine learning scenarios. Some testers might prefer lower latency, while others would sacrifice latency to achieve the higher level of throughput that their use case demands.

Testers can find latency and throughput numbers for each completed run in a JSON results file in the AIXPRT/Results folder. The test also generates CSV results files that are in the same folder. The raw results files report values for each AI task configuration (e.g., ResNet-50, Batch1, on CPU). Parsing and consolidating the raw data can take some time, so we’re developing a results file parsing tool to make the job much easier.

The results parsing tool is currently available in the AIXPRT CP2 OpenVINO – Windows package, and we hope to make it available for more packages soon. Using the tool is as simple as running a single command, and detailed instructions for how to do so are in the AIXPRT OpenVINO on Windows user guide. The tool produces a summary (example below) that makes it easier to quickly identify relevant comparison points such as maximum throughput and minimum latency.

AIXPRT results summary

In addition to the summary, the tool displays the throughput and latency results for each AI task configuration tested by the benchmark. AIXPRT runs each AI task multiple times and reports the average inference throughput and corresponding latency percentiles.

AIXPRT results details

We hope that this information helps to make it easier to understand AIXPRT results. If you have any questions or comments, please feel free to contact us.

Justin

Posted in AI, AIXPRT, benchmark, Benchmark metrics, Cross-platform benchmarks, image processing, Machine learning, object detection, OpenVINO, ResNet-50, SSD-MobileNet v1, Windows 10 | Tagged , , , , , , , , , |

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