Swissbit Solutions for Edge Computing and AI

The data processing and server landscape is under a tremendous transition. The introduction of edge servers with remote management for localized edge computing, edge-based artificial intelligence and machine learning, distributed IoT, and fog computing replaces more and more the traditional centralized cloud computing approach.

For the last decade, there has been a trend towards cloud computing. Benefits such as ease of access, cost efficiency, reliability, centralized maintenance, scalability of processing power and storage capacity, security against tempering, and easy migration convinced many companies to not just move their data, but also their applications into the cloud.

The more recent development of artificial intelligence and machine learning highlighted the limitations of centralized computing: latency, dependency on uninterrupted data connections, and security concerns.

If fast decisions need to be taken with the support of artificial intelligence the cloud approach is no longer working. Image processing like surveillance, object identification, and collision detection need processing of streaming data right at the data source or at least as close as possible. Content distribution needs to be spread out to many distributed caching servers. Edge computing offers processing power and near-by storage on powerful, but distributed platforms.

Cloud Computing versus Edge Computing

Benefits of Edge Computing

  1. Low latency and real-time operation: by bringing the processing power to where the decision needs to be taken, a quick response to requests can be guaranteed.
  2. Bandwidth reduction: the preprocessing of the data at the edge can discard all irrelevant data that otherwise would be transmitted to the cloud.
  3. Improved reliability and availability: in many cases the operation can be continued even when the data connection to the cloud is interrupted.
  4. Improved data privacy: companies with sensitive data can keep the access to their intellectual property strictly within their boundaries.

Edge Server requirements

The proximity to the data source also means that the servers are exposed to high or low ambient temperatures, to sterile or sensitive environments, or in case of transportation even to shock and vibration. All server components must be able to withstand these extreme conditions.

Edge servers are often designed with minimal or no active airflow within the system. Heat dissipation then occurs exclusively through convection. This mandates low-power memory components. Additionally, the topics of data security and encryption are becoming increasingly important.

Key storage product features

Storage products for edge servers need to fulfill the following requirements:

  1. Wide temperature range storage with low power consumption and moderate throttling.
  2. Midsize capacity storage with high sustained write data rate and low latency.
  3. Fully encrypted SSDs with remote management capability.
  4. High endurance SSDs for extended lifetime with enhanced lifetime monitoring.
  5. Highest quality SSDs with low DDPM rate for uninterrupted operation.
  6. Power-loss protected SSDs for operation under unreliable power delivery situations.
  7. Small form factor boot drives in addition to high density data drives.

Artificial Intelligence (AI) and Machine Learning (ML)

Artificial intelligence is the approach to implement human like intelligence to computing systems to allow them trained decision making or generation of content. The so-called AI cycle encompasses the various stages of creating, deploying, and maintaining artificial intelligence systems. Below there is a simplified representation of the different stages. This cycle is iterative, as models often need to be updated and refined based on new data, changing requirements, or evolving business needs.

The Stages of the AI Cycle

Problem
definition

Defining the problem and expected solution is crucial as it influences the model. Different AI types (generative, analytic, administrative) require distinct model architectures and learning processes.

Data
collection

Training AI necessitates a large collection of high-quality example data, stored on large capacity SSDs like Swissbit Enterprise SSDs, in data lakes.


Data
preparation

Collected data must be preprocessed to enhance or reduce it to the necessary details for model training. High-speed SSDs and high-capacity DRAM are essential for this phase.

Model
development

Key aspects include coverage, response time, size, accuracy, and flexibility. Model requirements determine the platform, especially for edge implementations where capabilities must match.


Model training
and evaluation

The model learns by comparing its results with correct answers, through automated or human training. High-performance AI systems with GPUs, TCUs, HMB memory, and fast SSDs are necessary. This stage may involve multiple cycles until the final model is achieved.

Model deployment
and monitoring

The final model often runs on a different platform from where data collection or training occurred, usually on edge servers. User interaction can help improve the model. Swissbit offers edge server SSDs with robust environmental endurance, low power consumption, and compact form factors.

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Why Swissbit ?

  • Competent and trustworthy partner: more than 20 years of experience with sophisticated storage and security solutions in the industrial market with „Made in Germany“ products.
  • Best quality products and solutions: Our products are designed for highest reliability, endurance and quality standards.
  • Unique technical support and customer relationship: Knowledgeable application engineers and technical sales available for product consultancy.

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