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Average Ratings 0 Ratings

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ease
features
design
support

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Write a Review

Description

This innovative tool is designed for quantizing convolutional neural networks (CNNs). It allows for the transformation of both weights/biases and activations from 32-bit floating-point (FP32) to 8-bit integer (INT8) format, or even other bit depths. Utilizing this tool can greatly enhance inference performance and efficiency, all while preserving accuracy levels. It is compatible with various common layer types found in neural networks, such as convolution, pooling, fully-connected layers, and batch normalization, among others. Remarkably, the quantization process does not require the network to be retrained or the use of labeled datasets; only a single batch of images is sufficient. Depending on the neural network's size, the quantization can be completed in a matter of seconds to several minutes, facilitating quick updates to the model. Furthermore, this tool is specifically optimized for collaboration with DeePhi DPU and can generate the INT8 format model files necessary for DNNC integration. By streamlining the quantization process, developers can ensure their models remain efficient and robust in various applications.

Description

We created Tenstorrent DevCloud to enable users to experiment with their models on our servers without the need to invest in our hardware. By developing Tenstorrent AI in the cloud, we allow developers to explore our AI offerings easily. The initial login is complimentary, after which users can connect with our dedicated team to better understand their specific requirements. Our team at Tenstorrent consists of highly skilled and enthusiastic individuals united in their goal to create the ultimate computing platform for AI and software 2.0. As a forward-thinking computing company, Tenstorrent is committed to meeting the increasing computational needs of software 2.0. Based in Toronto, Canada, Tenstorrent gathers specialists in computer architecture, foundational design, advanced systems, and neural network compilers. Our processors are specifically designed for efficient neural network training and inference while also capable of handling various types of parallel computations. These processors feature a network of cores referred to as Tensix cores, which enhance performance and scalability. With a focus on innovation and cutting-edge technology, Tenstorrent aims to set new standards in the computing landscape.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

Docker No 
Inworld TTS No 

Integrations

Docker Yes 
Inworld TTS Yes 

Pricing Details

$0.90 per hour
Free Trial No 
Free Version No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

DeePhi Quantization Tool

Website

aws.amazon.com/marketplace/pp/prodview-bwtx6kzwg3gva

Vendor Details

Company Name

Tenstorrent

Country

Canada

Website

tenstorrent.com/devcloud/

Product Features

Product Features

Artificial Intelligence

Chatbot No 
For Healthcare No 
For Sales No 
For eCommerce No 
Image Recognition No 
Machine Learning No 
Multi-Language No 
Natural Language Processing No 
Predictive Analytics No 
Process/Workflow Automation No 
Rules-Based Automation No 
Virtual Personal Assistant (VPA) No 

Machine Learning

Deep Learning No 
ML Algorithm Library No 
Model Training No 
Natural Language Processing (NLP) No 
Predictive Modeling No 
Statistical / Mathematical Tools No 
Templates No 
Visualization No 

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