The Current Times

In the current status of the world, the grocery retail industry has been thriving, with the average increase of sales being 26.9% across the board. Although this is great news for the grocery retail industry, there is a popular saying that goes, “more money, more problems”. So as sales may increase for businesses across this sector, so will their shrink rate, extended queues at tills, staffing needs, etc.

In the grocery retail industry, shrinkage, or shrink, is the term used to describe a reduction in inventory due to shoplifting, employee theft, administrative/human errors such as unintentional/intentional record keeping, pricing, etc…

Episode 1: Teaching the AI to train itself

This is the first part of a three part series looking at why retailers struggle to adopt AI technology, what can be done to help them and why it’s important for tech companies to focus on making the processes easier, while directing all their efforts to improving business outputs.

Artificial Intelligence (AI) isn’t a new concept: for years, Hollywood has been throwing the name around to make its sci-fi blockbusters more futuristic. Now AI’s application has firmly found itself in the real world, and recently seems to be making a huge impact in many industries. …

En route to eradicating the need for cloud in any AI training and retraining processes. Part 2 of 3.

Live view of our cluster of edge devices, running a live distributed and collaborative training.

If you haven’t read the first post in this series, we suggest that you read it in order to get the most benefit from this article. It described our process for building a cluster of 100 edge devices, in order to train deep-learning and machine-learning models in a distributed manner using a cluster of edge devices, without requiring the use of the cloud while achieving near-perfect accuracy. …

Fresh Produce Recognition at Point of Sale

Trend Spotting

Coming back from NRF 2020 we’ve spotted the next big trend in grocery retail:

Image Recognition of Barcode-less Items

Self checkouts will soon be using computer vision technology to recognise barcode-less items such as fruits and vegetables. No more frustrated customers browsing through endless screens to locate the Banana’s SKU or delaying the queue trying to distinguish between Pink Lady and Red Delicious apples.

At first, we were cautious about calling this a trend, but one after the other, we noticed major industry leaders investing in initiatives aimed at this vision. NCR, Toshiba, Zebra, Shekel, DataLogic, Fujitsu, HP, NEC, StrongPoint…

Edgify’s Collaborated Method for Distributed Learning, to be Fully Released at NeurIPS this Year!


There is a growing interest today in training deep learning models on the edge. Algorithms such as Federated Averaging [1] (FedAvg) allow training on devices with high network latency by performing many local gradient steps before communicating their weights. However, the very nature of this setting is such that there is no control over the way the data is distributed on the devices.

consider, for instance, a smart checkout scale at a supermarket that has a camera mounted on it and some processing power. You want…

Check Out the Check-in Process.

“You don’t build a business — you build people —and then people build the business”. — Zig Ziglar

A little over a year ago, the time came to put our money where our mouth is when we said that we are a “people-first,” startup company. This forced us to start asking ourselves and our employees more difficult questions on how we’re doing at attracting and retaining the best people, and how we can improve in doing so as a company.

On one hand, we decided it was time for our company to embrace some type of (formal) review and feedback…

En route to replacing the cloud for all AI training. A three part series on setting up your own cluster of edge devices (1/3).


This is the first post, of a three-part series, describing how we built a cluster of 100 edge devices, in order to train deep learning and machine learning models without ever using the cloud, whilst achieving close to perfect accuracies.

These 100 edge devices, are intended to replicate real world edge devices, such as self checkout POS, cameras, connected cars, medical devices, etc..

This first post focuses on the various aspects that we considered while building our edge-device clusters, including hardware, network limitations, power supply requirements, the actual construction of the cluster and more. The next post of this series…

(3/3) an Edgify Research Team Publication

In the first post of this series, we presented two basic approaches to distributed training on edge devices. In the second, we explored compression methods for those approaches. Diving deeper into the real-world challenges of these training methodologies, we now introduce a problem with common architectures that arises from certain common data distributions — and the solution we have found for it.

Non IID Data Distribution

Training at the edge, where the data is generated, means that no data has to be uploaded to the cloud, thereby maintaining privacy. This also allows for continuous, ongoing training.

That being said, keeping things distributed, by its…

(2/3) An Research Team Publication

In the first post of this series we presented two basic approaches to distributed training on edge devices (if you havent read our first post in this series, you should start here). These approaches provide benefits such as AI data privacy and the utilization of the compute power of edge devices. Large-scale distributed training systems, however, may fail to fully utilize that power (as we’ve come to see in our own experiments), as they consume large amounts of communication resources. As a result, limited bandwidth can impose a major bottleneck on the training process.

The two issues described below, pose…

How many times have we heard sentences like: “this is how we have always done it” or “everyone else does it this way” ?

Working in teams has a lot of advantages. We share tasks, ideas, goals and most importantly a sense of belonging. To sustain and maintain a working group structure, the team must form a set of unwritten rules for each member to follow. Any new member joining the group must follow these rules or the group might treat the new member as an outsider. In other words, to be an efficient member of the group — that person has to conform to the group’s rules.

Conformism is a very efficient tool in helping the group reach its common goals. However, with…

A foundational shift in the world of AI training. Deep Learning and Machine Learning training directly on edge devices.

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