Data rich Computing: m.2 meets AI at the edge

Data explosion —— And the applications it drives —— Is creating earth shaking changes . These transformations require specialization to create systems that are more capable of interacting with AI workloads , Efficient and on-demand .

For system designers , This means that yesterday's performance acceleration strategy may no longer meet the requirements . Although based on CPU/GPU The design of helps alleviate the slowdown of Moore's law , But these processor architectures now find it difficult to keep up with the real-time data requirements inherent in automation and reasoning applications . This is especially true in more stringent non data center scenarios . In addition, the challenge of meeting the requirements of cost performance and power consumption is increasing , Performance acceleration is more important than ever to consider Computing 、 Storage and connectivity . All these factors are necessary to effectively integrate workloads close to the point of data generation , This is true even in harsh environments where environmental challenges are detrimental to system performance .

This is it. M.2 Where shape accelerators play a role in removing performance barriers in data intensive applications . As a powerful design option ,M.2 Accelerators provide domain specific value to system architects , To match AI The exact requirements of the workload . And use CPU/GPU Compared with similar systems of Technology , be based on M.2 Our system can be faster 、 Manage reasoning models more efficiently . These increases drive innovative system design , It is very suitable for rugged edges. More systems are deployed in challenging non-traditional scenes , And specially built systems offer great opportunities . ad locum , General purpose embedded computers are designed by utilizing M.2 There is a clear difference between computers with more modern acceleration options such as acceleration modules to deal with reasoning algorithms .

M.2 Also known as the next generation boundary dimension interface , Developed by Intel , Designed to provide flexibility and powerful performance .M.2 Support multiple signal interfaces , For example, serial ATA (SATA 3.0)、PCI Express（PCIe 3.0 and 4.0） and USB 3.0.M.2 The expansion slot has a variety of bus interfaces , Highly adaptable to accelerators with different performance 、 Storage agreement 、I/O Expansion module and wireless connection .

M.2 Also through support SATA and NVMe（ Nonvolatile memory is fast ） Storage protocols provide traditional and modern compatibility . Traditional standards SATA Including advanced host controller interface (AHCI), Intel defines it as passing HDD（ Hard drive ） Rotating metal disks in storage optimize storage protocols for data operations .NVMe An alternative is provided , To make the most of NAND chip （ Flash memory ） Storage and PCI Express Channel enables extremely fast solid state drives (SSD) Storage .

Performance accelerator also uses M.2 shape , Benefit from its powerful and compact interface . These include AI Accelerator 、 Memory accelerator 、 Deep learning Accelerator 、 Reasoning accelerator, etc . This dedicated processor is dedicated to AI The workload , Provide higher power performance ratio .

Unlock through real-time data performance in more environments AI

Data is the key to business innovation today , More importantly, it provides the ability of cognitive machine intelligence . Whether it is to supply power for advanced telematics or intelligent kiosks in the factory workshop , Or provide power for passengers and monitoring services in infrastructure such as railway stations and airports , The data is right around us , When it can be found 、 Capture 、 Evaluate and use for immediate .

Countless industries are eager to take full advantage of data to create new services and enhance business decisions , But in many strict industrial environments , It is too inefficient to handle small automated or AI tasks at the data center level , Cannot provide real value . Due to overuse of Computing 、 Bandwidth and storage resources （ Although it is necessary ）, The power consumption and cost of this traditional centralized computing structure are too high . Besides , High latency means performance will be affected , And the lack of data privacy can be a headache .

Data growth is combined with the complexity of the edge computing environment , Is pushing AI Computing framework from general CPU/GPU The option shift is based on using M.2 A dedicated accelerator for standard domain specific architectures —— These options are smaller 、 More energy efficient . This is a response to complexity 、 Strategies for real and persistent data challenges . As the number of IOT and industrial IOT devices increases , The amount and speed of data they generate are also increasing . Application designers and developers must recognize , There is an urgent need for performance acceleration that is closer to the data source and built specifically for the task at hand - Especially when deploying edge computing hardware to cope with data processing and reduce the burden of data centers and data centers .