Interest in the Edge is driving the need for Edge AI hardware
Specialized Edge AI hardware, also known as AI accelerators, accelerates data-intensive deep learning inference on Edge devices, making them an appealing option for many compute-intensive tasks.
With the growing demand for real-time deep learning workloads, specialized Edge AI hardware that allows for fast deep learning on-device has become increasingly necessary.
On top of this, today’s standard (cloud-based) AI approach isn’t enough to cover bandwidth, ensure data privacy, or offer low latency. Hence, AI tasks need to move to the Edge.
As a result of this movement, recent Edge AI trends are driving the need for specific AI hardware for on-device machine learning inference.
Edge AI hardware explained
Based on the current situation, Gartner expects that by 2025, 30% of new industrial control systems will include analytics and Edge AI inference capabilities. In the same timeframe, Garter anticipates that 75% of enterprise-generated data will be created outside a centralized data center.
This shows how distributed IT systems are advancing quickly in enterprise spaces and showcases how an upward trend is pushing technology to the Edge.
When enterprises initially deployed embedded systems, system architects had no idea that the volumes of data created by IoT and AI workloads today would be huge.
For example, one smart factory can quickly clear about 1 petabyte of daily data. That includes data generated from warehouse managing systems, sensors, machine vision equipment, and manufacturing equipment.
As the environment and conditions changed, outdated embedded systems from a decade ago needed change to prop up new demand for on-device Edge AI.
Today, much of this AI work is completed in the cloud, but extreme amounts of data are putting pressure on centralized cloud systems.
If data were processed at the Edge instead, issues regarding high bandwidth usage, network traffic, and latency would be resolved. Moving AI to the Edge would also come with security, energy efficiency, reliability, and cost-saving benefits.
To achieve this, hardware must adapt to cater to Edge AI.
Enter AI accelerators.
AI accelerators, a form of specialized AI hardware, are made to speed up or accelerate data-intensive deep learning inference, making them perfect for use on Edge devices.
How does Edge AI hardware work?
In a nutshell, hardware acceleration moves the most power-intensive parts of a model to specialized hardware. This hardware is geared towards giving the AI model an extra processing boost while at the same time lowering the amount of power consumed.
Currently, two clear accelerator spaces exist: the data center and the Edge.
Regarding the data center space, chip sizes are getting bigger since data centers require greatly scalable architectures.
This is especially true in the case of hyper-scale data centers, which house critical computing resources and network infrastructure used by the likes of Google and Amazon.
Edge AI can be thought of as the opposite end of the spectrum. Energy efficiency is vital when talking about the Edge, and space is limited.
The good news is that AI accelerators can be integrated into Edge-enabled devices, which can deliver near-instantaneous results no matter how small.
The top 4 benefits of running an AI model at the Edge
AI accelerators incentivize moving from the cloud to the Edge as they allow AI models to run efficiently, often without any need for the cloud.
But what are the motivations for relocating AI processing to the Edge in the first place?
The answers can be found by exploring the top 4 benefits that come from running AI at the Edge.
1. No more latency
With local processing, communication delays aren’t an issue as data doesn’t need to travel to be analyzed or for an Edge AI model to derive a meaningful course of action.
2. Bandwidth costs are down
Once communication between devices and the cloud is minimal, costs go down.
3. Dependable AI work without a connection
When data is collected and processed on-device, avoiding network issues is easy. Now there’s no need to rely on a stable internet connection or a connection to the cloud.
4. Fewer privacy issues and better security
Data is less likely to get stolen, leaked, or intercepted since there isn’t a need to connect to the cloud.
The major benefits of utilizing Edge AI hardware
Now that the reasons for moving AI to the Edge have been explored let’s examine the benefits of Edge AI hardware in more detail.
Edge AI hardware offers greater scalability
Writing an algorithm to solve a problem doesn’t come easy.
It’s even harder to take that algorithm and effectively break up the workload along numerous cores (independent units within a processing chip) to get greater processing capabilities.
AI accelerators excel at this and enable impressive performance and speed enhancements while they’re at it.
Edge AI hardware improves energy efficiency
AI accelerators can be up to 1,000x more efficient than general-purpose compute machines!
When used in an Edge application with a low power budget, AI accelerators don’t generate too much heat or draw on much power, even when performing extensive calculations.
Edge AI hardware offers a heterogeneous architecture
AI accelerators allow certain systems to accommodate multiple specialized processors that perform specific tasks, thus providing the computational capabilities that AI applications often require.
Edge AI hardware increases computational speed and reduces latency
AI accelerators cut down the time it takes to devise a solution to a problem. This is because AI accelerators enable on-device AI to work extremely quickly. This low latency is vital when it comes to mission-critical applications.
4 Examples of Edge AI hardware at work
Now that Edge AI Hardware has been explained in detail, let’s explore 4 use cases where AI accelerators have proven instrumental.
1. Edge Robotics
Hardware innovation is making robotics at the Edge possible.
For example, the hardware within embedded robotics controllers can help robots communicate with each other and the world around them.
2. AI Machine Vision
When the correct Edge AI hardware is used, devices can take advantage of the minimal weight and power requirements necessary to perform complex tasks like those involving machine vision.
Embedded, high-performance CPUs, VPUs, and GPUs help to get machine vision tasks done expertly.
On top of that, latency and bandwidth issues no longer exist since all computing power is located at the Edge.
3. Deep Learning Acceleration Platforms (DLAPs)
DLAPs enable machines to enhance their performance and make decisions on their own.
They offer quick responses, improved control, and enhanced security to keep operations running at their best.
DLAPs offer functionalities, including image analysis, image pre-processing, data acquisition, and AI acceleration.
4. AI on Modules
MXM, or Mobile PCI Express Module GPUs, make it possible for AI to function in graphic cards the size of a palm. They offer high performance per watt.
What’s more, they are made to work well under extreme conditions.
These modules can run in extreme temperatures, small spaces, dusty or corrosive surroundings, and spaces with little to zero ventilation.
Different types of AI hardware accelerators
In the interest of conserving processing power and considering the model size and weight, there are many AI accelerators that don’t need a single large chip. Some of these are:
- Spatial accelerators such as TPUs (Google’s Tensor Processing Units)
- Multi-core superscalar processors
- GPUs (Graphics Processing Units)
All of the above are separate chips that can be combined in a system, allowing larger neural networks to run efficiently.
CGRA, or Coarse-Grain Reconfigurable Architectures, is also steadily growing in this space. The main reason is that they present appealing tradeoffs between energy efficiency and performance.
Let’s explore other processor types for AI workloads at the Edge (as well as some that have already been mentioned) in more detail:
A Central Processing Unit (CPU) is a processing unit of up to 4-16 cores used for general purposes.
These units are ideal for mixed data inputs, like systems that extract, transform, and load processes.
CPUs can handle system management and run complex tasks.
A Graphics Processing Unit (GPU) is a processing unit containing hundreds to thousands of parallel cores used for rendering high-speed graphics.
Due to the number of small cores, GPU units are perfect for Edge AI workloads since they can enable timely Edge AI inference and neural network training. These units leave a more significant footprint since they consume much more power than CPUs, but it’s important to note that they enable high-performance processing.
A Field- Programmable Gate Array, or FPGA, is a logic gate that can be configured.
These units are an excellent option for users looking for a high level of flexibility. In addition, FPGAs don’t use as much power as CPUs or GPUs.
Engineers who are programming experts typically use FPGA for in-field reprogramming.
An Application Specific Integrated Circuit (ASIC) has good speed and consumes little power.
The trouble is that these AI accelerators take a long time to design, which makes them more expensive than the other accelerators discussed above.
With that in mind, ASICs are suitable for products expected to run in massive volumes.
Types of ASICS include TPUs and vision processing units (VPUs).
AI accelerators are a must for well-performing Edge AI applications
As mentioned, some computation tasks are highly data-intensive. This makes utilizing AI hardware acceleration for Edge devices beneficial, especially regarding speed.
Since speedy processing is essential for AI apps, AI accelerators are instrumental in providing these near-instant results.
This makes AI accelerators a valuable asset to the Edge AI space.