ARM to Revolutionize AI, IIoT: HashCash

ARM authorities maintain that the improvement in data throughput in ARM embedded architectures will make a huge difference in expanding the capabilities of edge AI platforms.

In the next 5 years, the Industrial Internet of Things (IIoT) projected to disrupt. We had previously reported how the number of IoT devices has grown exponentially within a short while and how it could continue to do so generating a vast amount of data to feed into the Machine Learning and AI departments.

Also Read: Future is the coupling of IoT and AI: Here’s Proof

Now, these IoT devices certainly came with their fair share of troubles. Home or office IoT devices are vulnerable to invasion by threat entities. Once out of the manufacturing unit, these devices are almost never upgraded or patched. This is true for devices built for home use, car sensors, and even aircraft sensors.

Also, the devices have huge power requirements leaving very little for data, thus not meeting remote functioning and sufficiency for a fully-functioning Industrial IoT device.

Understanding ARM Architecture

In strictly technical terms, ARM stands for “Advanced RISC Machine.” It is a type of RISC (Reduced Instruction Set Computer) architecture that is increasingly gaining relevance in the modern world.

Source: https://www.watelectronics.com/arm-processor-architecture-working/

Fewer transistors are required in RISC architecture rendering a less expensive and less power-hungry alternative. An Important factor that supports such a claim is its simple design using a stark 5 stage pipeline. But, other contributing factors are as follows below.

• ARM makers apply an instruction set called Thumb, which accepts 32-bit instructions and compresses them down to 16-bits. This feature enables programs to be coded much more densely than standard RISC instruction sets, also trimming some portions of the hardware down in size.
• Thumb-enabled processors also allow 32-bit instructions to run on the same processor. In fact, 16-bit and 32-bit instructions may be mixed together and the hardware will still be able to decode and decompress at the same time without a performance hit, thus maintaining powerful computing capabilities.
• Cost is minimized owing to a simple, small structure with many configurations available. Small structure implies less silicon, higher yield per wafer.
• A simple pipeline and instruction set makes it easily learned, optimized, and built, again saving on cost.

What Makes ARM Microprocessors So Popular?

ARM is developed and licensed by Arm Holdings, Inc.  What is interesting about this business model is that, unlike other processor companies, Arm does not physically manufacture the processors. Rather they design the core ARM architecture and hold intellectual property and license them to serve as a blueprint with the flexibility to be built as per the IoT developer’s need.

Also Read: HashCash Journals the Move Towards Hyper-Automation

ARM For IIoT

With the ARM architecture, IIoT developers are not constrained to build non-integrated peripherals around a predefined chipset but with the license in hand, they can develop unique Systems-on-Chip (SoC) and Systems-on-Modules (SoM) that aren’t restricted to any single Central Processing Unit (CPU). Rather, they are free to directly build end products around whichever processors and peripherals they choose using ARM architectures.

During a time when agility and customization are critical, a model, such as this, can break down many of the barriers to IIoT transformation. ARM’s widespread adoption alludes to its effectiveness and value for major IoT undertakings. 

ARM architecture development finds support from an active, robust global development community. Many would consider this, the single most attractive feature to the ARM architecture and the integrated chipsets it enables. 

To summarize from above, the reasons, why ARM is the good choice for IIoT are:

1. Low Power Consumption
2. Integrated Components
3. A Global Support Community

ARM For AI

The requirement of Edge AI is clear. We want AI to process on the device itself, rather than some remote server. This should gain us privacy and speed while handling the requests.

Rather than designing processor chipsets meant for phones and tablets, ARM designs are aimed at bringing AI to more IoT devices than would otherwise be possible. 

One use case of this application could be a walking stick with a 360-degree viewing camera that would detect obstacles and send off alerts from a distance.

Finally…

ARM authorities maintain that the improvement in data throughput in ARM embedded architectures will make a huge difference in expanding the capabilities of edge AI platforms. 

A lot of breakthroughs are expected by the mid of 2022 in this sector if not further impeded by the want of silicon or the pandemic.