A recent series of tweets by the Magic AI team has made me curious about this technology. The premise is simple: it can help you create a virtual reality game. And it’s also backed by science. In this article, I’ll explain how this technology works, as well as the issues it presents.
Graphs:
Graphs are data structures that represent connections. While conventional databases tend to focus on rows and columns, graphs capture relationships in a more faithful way. Consider a dataset that contains information about movies, where the nodes are the actors and the edges represent directors and films. It might be impossible to represent all of this data in a traditional database, but graphs help.
Graphs are an increasingly mainstream technology trend. Gartner recently named graphs as one of its top 10 data and analytics technology trends for 2021. Today, they play a crucial role in everything from master data management to tracking laundered money to connecting Facebook friends. Graph data is also the backbone of the dominant search engine’s page ranker. Graphs are used by NASA engineers and Panama Papers researchers, and even by Fortune 500 companies.
Neural Magic is an open-source platform that lets data scientists leverage compute resources without specialized hardware. Designed to take advantage of the unique structure and sparsity of deep learning models, Neural Magic delivers breakthrough performance without sacrificing accuracy. Using its platform, data scientists can accelerate their research by using artificial intelligence and machine learning.
78 AI and Computer Vision tests performed by neural networks on your smartphone
This benchmark consists of 78 computer vision and AI tests that measure over 180 different aspects of a neural network’s performance. The benchmark considers neural networks that utilize a wide variety of architectures and approaches to optimize performance. Below is a description of each section of the benchmark. The DeepLab-V3+ network is an example of a powerful small neural network that is designed for mobile devices with limited resources. The network can detect up to 1000 object classes from a single photo.
Performance gains with pruning and recalibration
Pruning and recalibration are two common ways to improve the performance of neural networks. Compared to non-pruned networks, pruned neural networks can be as fast as 8 times faster and smaller than their unpruned counterparts. The authors use a pre-training model that combines pruning with sparcing to create general sparse architectures with high compression.
Pruning is the process of minimizing loss by modifying the sensitivity coefficients of each layer of the model. This technique requires a small calibration set. After training, the procedure outputs a sparsity profile. This is the most common and effective method of minimizing loss in deep residual learning. This method also works well on AMD CPUs, which lack quantization support.
The authors of this paper describe an iterative pruning method that preserves the flow of synaptic strengths within an initialized network. It also has a data-agnostic requirement and outperforms other pruning methods at init baselines. This theoretical result has been translated into a practical algorithm that can be used to improve neural networks. This new approach will be a major step towards enhancing neural network performance.
Pruning is a common optimization method that helps reduce network size and improve resolution performance. It can be performed during training or after convergence. Pruning allows the network to readjust and maintain a high performance. It can be used to enhance the density and speed of sparse networks.
There are numerous heuristics that can score how important a weight is in a network. One common rule is that larger weights should be pruned less. This rule, however, contradicts L2-weight regularization, which penalizes large weights. Other, more involved techniques try to learn pruning masks based on gradient-based methods or higher order curvature information.
Moffett AI’s MLPerf
The benchmarking activity of ML inference is incredibly complicated, with constant changes in variables. The goal of MLCommons is to create a standardized way to measure performance and track progress. The benchmarking tool pulls in publicly available data, provides the code for the ML network model and a target quality score.
The MLPerf benchmark is designed to stress machine learning models, software and hardware to boost speed and efficiency. The benchmarks are open-source and peer-reviewed to foster innovation, performance, and energy efficiency in machine learning. Its latest version, MLPerf Inference, measures the speed of AI inference across multiple systems. The benchmark also includes new division and object detection models.
The MLPerf benchmark is a competition that examines the speed and capabilities of different artificial intelligence (AI) systems. It includes a number of benchmarks, including a closed category for data centers, which is the most competitive.
The MLPerf benchmarking tool is a popular benchmark for AI performance. It includes five thousand performance and power measurements submitted by 21 organizations. Among the results, Nvidia dominated the MLPerf v2.1 Inference benchmark. Despite its dominance, Qualcomm and other chipmakers impressed on both performance and power metrics.
The MLPerf benchmark is designed to increase AI inference speeds by allowing AI systems to run faster on GPUs. It has improved AI inferencing speeds by up to 6x, according to the company. Inspur Information’s AI computing systems are highly efficient and have a wide range of optimization capabilities.
The benchmarking tool is a collaboration between Nvidia and HPE. It’s aligned with HPE’s edge-to-cloud and heterogeneity goals. The company developed the software in collaboration with Nvidia and Qualcomm Technologies.
OctoML
OctoML is a machine learning platform aimed at increasing AI inference speeds. It offers a complete software library, including key machine learning frameworks and acceleration tools like Apache TVM. It also features accelerated computing capabilities, making it easier to deploy models across different devices. The company has raised over $43 million in funding so far. It has employees located across the United States.
The platform is aimed at boosting AI inferencing speeds for businesses by reducing the manual work involved in developing AI models. It integrates with existing application stacks to eliminate dependencies and deliver production-ready functions. Customers will be able to use OctoML in a variety of ways, including on-premise and cloud environments.
OctoML’s next phase will focus on building a scalable, IoT-based connection to a long-term model memory. This will communicate with a pattern classifier that can be configured for either cluster or rule-based processing. The system also uses an edge pipeline, which can be configured to match cross-section data requirements.
Conclusion
OctoML uses a multi-layer architecture with the MobilenetV2 architecture. Each convolution layer is connected to a Squeeze-and-Excitation (SE) block. Its base expansion factor is t0. The algorithm performs a pointwise convolution, followed by a depthwise convolution. Then, a second pointwise convolution is connected to a low-dimensionality bottleneck block.
