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What is SiMa?

SiMa offers a state-of-the-art, software-centric embedded edge machine learning system-on-chip (MLSoC) platform designed to deliver efficient and high-performance AI solutions across a variety of applications. This MLSoC expertly integrates multiple modalities, including text, images, audio, video, and haptic feedback, enabling it to perform complex ML inferences and produce outputs in any of these formats. It supports a wide range of frameworks, such as TensorFlow, PyTorch, and ONNX, and can compile over 250 diverse models, guaranteeing users a seamless experience coupled with outstanding performance-per-watt results. Beyond its sophisticated hardware, SiMa.ai is engineered for the comprehensive development of machine learning stack applications, accommodating any ML workflow that clients wish to deploy at the edge while ensuring both high performance and ease of use. Additionally, Palette's built-in ML compiler enables the platform to accept models from any neural network framework, significantly enhancing its adaptability and versatility to meet user requirements. This impressive amalgamation of features firmly establishes SiMa as a frontrunner in the ever-evolving realm of edge AI, ensuring customers have the tools they need to innovate and excel. With its robust capabilities, SiMa is poised to redefine the standards of performance and efficiency in AI-driven applications.

What is Fabric for Deep Learning (FfDL)?

Deep learning frameworks such as TensorFlow, PyTorch, Caffe, Torch, Theano, and MXNet have greatly improved the ease with which deep learning models can be designed, trained, and utilized. Fabric for Deep Learning (FfDL, pronounced "fiddle") provides a unified approach for deploying these deep-learning frameworks as a service on Kubernetes, facilitating seamless functionality. The FfDL architecture is constructed using microservices, which reduces the reliance between components, enhances simplicity, and ensures that each component operates in a stateless manner. This architectural choice is advantageous as it allows failures to be contained and promotes independent development, testing, deployment, scaling, and updating of each service. By leveraging Kubernetes' capabilities, FfDL creates an environment that is highly scalable, resilient, and capable of withstanding faults during deep learning operations. Furthermore, the platform includes a robust distribution and orchestration layer that enables efficient processing of extensive datasets across several compute nodes within a reasonable time frame. Consequently, this thorough strategy guarantees that deep learning initiatives can be carried out with both effectiveness and dependability, paving the way for innovative advancements in the field.

Media

Media

Integrations Supported

PyTorch
TensorFlow
Bloomberg
Caffe
Kubernetes
ONNX
Torch

Integrations Supported

PyTorch
TensorFlow
Bloomberg
Caffe
Kubernetes
ONNX
Torch

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

SiMa

Company Location

United States

Company Website

sima.ai/

Company Facts

Organization Name

IBM

Date Founded

1911

Company Location

United States

Company Website

developer.ibm.com/open/projects/fabric-for-deep-learning-ffdl/

Categories and Features

Artificial Intelligence

Chatbot
For Healthcare
For Sales
For eCommerce
Image Recognition
Machine Learning
Multi-Language
Natural Language Processing
Predictive Analytics
Process/Workflow Automation
Rules-Based Automation
Virtual Personal Assistant (VPA)

Categories and Features

Deep Learning

Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization

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