Appleβs macOS 27 Golden Gate software update is closer than ever to being released to the public later this month. Now, developers can download the first Release Candidate update via either the OTA method.
The modern digital ecosystem has undergone a silent but total transformation. Every day, we interact with ARM-based processors billions of times. These chips drive almost all iOS and Android devices and are key to the significant performance improvements seen in Appleβs M-series Macs. Some lightweight notebooks, such as Chromebooks, use ARM processors. IoT devices are largely powered by ARM. Besides that, recently ARM expanded into silicon production with the Arm AGI CPU, its first production-ready silicon designed for agentic AI workloads in data centers. With this level of ubiquity in our digital world, itβs important to be familiar with ARM.
Therefore, this article serves as a foundation for learning about ARM. It delves into the architecture of ARM CPUs, covering design principles and energy efficiency. Letβs get rolling!
What is ARM?
ARM is a family of CPU designs based on a simple, efficient instruction set (RISC). It started as βAcorn RISC Machineβ, then βAdvanced RISC Machinesβ, and now itβs just called ARM.
Unlike traditional chipmakers, Arm Holdings does not manufacture physical processors. Instead, the company designs the foundational CPU architecture and licenses its intellectual property and processor cores to other hardware manufacturers (such as Apple and Nvidia).
What is an ARM-Based CPU?
ARM CPUs use a simple, efficient RISC instruction set. RISC stands for Reduced Instruction Set Computer. It represents a hardware design philosophy focused on streamlining how a processor interprets and executes software instructions.
This design philosophy stands in direct contrast to CISC (Complex Instruction Set Computer), which is the architecture utilized by traditional Intel and AMD x86 processors.
The RISC concept originated in the early 1980s, heavily influenced by research at the University of California, Berkeley. Researchers evaluating resource usage discovered that most software programs only utilized a small fraction of a processorβs complex, built-in instruction set. They realized that if they removed the highly complex, rarely used, and difficult-to-implement instructions, the remaining simpler instructions could execute much faster, while requiring significantly less physical space and power on the silicon chip. This discovery led directly to the development of early RISC designs, including the foundational Acorn RISC Machine (ARM) project in 1983.
Core Principles of RISC Design
RISC architectures use a fixed instruction width for high-speed execution. Unlike CISC architectures that have instructions of varying lengths, a modern 64-bit RISC architecture like ARM64 uses a uniform instruction size, typically 32 bits. This consistency makes it easier for the processor to identify where one instruction ends and the next starts, which helps in quickly fetching, decoding, and executing instructions.
A key feature of RISC design is its Load-Store architecture. In traditional CISC, a single instruction might perform operations directly on data in memory. In RISC, memory access and calculations are separate. In a RISC CPU, Arithmetic Logic Unit (ALU) operations only happen between registers, which are small, fast storage spaces on the processor. To work with data from memory, the processor has to first load it from RAM into a register, perform the calculation in the register, and then store the result back to memory.
To meet the needs of this Load-Store model, RISC processors have a large, uniform register file. Since data cannot be processed directly in memory, the CPU needs many registers to keep temporary data readily available. A 64-bit RISC processor usually has 31 general-purpose 64-bit registers that act as a quick local workspace.
The clear and register-focused design leads to mostly single-cycle execution and effective hardware pipelining. Because RISC instructions are straightforward and mainly work with registers, most can finish in one clock cycle. This single-cycle capability enables the processor to use an instruction pipeline. In this system, while one instruction is executed, another is decoded, and a third is fetched from memory simultaneously. This overlap helps the processor complete a new instruction nearly every clock tick, maximizing efficiency.
Feature / Approach
CISC (e.g., x86)
RISC (e.g., ARM)
Instruction complexity
Single instructions perform multiple tasks (data manipulation, memory access, arithmetic)
Breaks tasks into multiple simpler instructions
Execution example
One instruction: load β compute β store
Three separate instructions: load β compute β store
Decoding logic
Intricate and complex
Simpler, more uniform
Clock cycles per instruction
Often multiple cycles
Usually one cycle per simple instruction
Hardware requirements
Substantial hardware for decoding and execution management
Less hardware for decoding, more uniform control logic
Power & design impact
Higher power consumption and design complexity
Lower power consumption, simpler design
Optimization
Harder to optimize individual operations
Easier to optimize each step independently
Parallel execution
More difficult
Easier to achieve
Energy Efficiency
Firstly, at the core of the RISC philosophy is the use of a smaller vocabulary of simpler, fixed-length instructions. Because the CPU does not have to parse highly complex, variable-length instructions, the physical hardware required to decode and execute instructions is dramatically simplified. This simplicity results in a vastly reduced transistor count. For example, early ARM cores required only 30,000 to 35,000 transistors. Fewer transistors mean that fewer components are active during each instruction cycle, which directly lowers dynamic power consumption and dynamic leakage.
Secondly, RISC processors are designed to scale their power draw dynamically based on the active workload. Through techniques like Dynamic Voltage and Frequency Scaling (DVFS), the processor automatically lowers its operating voltage and clock speed during periods of low computational demand, conserving energy when peak performance is unnecessary. For example, microcontroller-class processors like the ARM Cortex-M series are engineered to draw almost zero power when in deep sleep states, yet they can wake up and execute tasks rapidly on demand.
Thirdly, on a system-on-chip level, modern RISC implementations leverage heterogeneous processing, such as Arm big.LITTLE and DynamIQ technologies. Instead of running all tasks on identical, power-hungry cores, the processor combines:
LITTLE cores: Tiny, ultra-efficient cores optimized to handle routine, low-intensity background tasks (like texting, email, or playing music) using minimal power.
big cores: High-performance cores designed to tackle heavy, sustained workloads (like mobile gaming or intense web browsing).
This dynamic, on-demand task allocation ensures that the high-power βbigβ cores are only activated when strictly necessary, maximizing overall battery life.
Apple M-series Chips
The Apple M-series chips are a group of processors made by Apple Inc. They are designed for efficient performance and are based on ARM architecture. Each chip includes a CPU, GPU, a Neural Engine for machine learning, and a unified memory system that helps improve overall efficiency.
Apple announced its move to its own M-series chips at the Worldwide Developers Conference (WWDC) on June 22, 2020. This change was from Intelβs x86 processors to ARM-based designs for better power efficiency and performance.
For example, the M1 chip offers up to 3.5 times faster CPU performance while consuming less power than Intel chips for certain tasks. This allows for high performance without generating too much heat.
The M-series chips also improve battery life. Devices often run up to 1.5 times longer than Intel-based Macs. This is due to their optimized power management. In real-world use, like watching videos or doing light work, the MacBook Air can last 15 to 18 hours, compared to the 11 to 12 hours typical of similar Intel models.
By 2026, devices like the Mac Studio and Mac Mini are using M-series CPUs to run advanced AI models directly on usersβ desks. Many people are shifting away from paying for AI services and choosing local systems instead.
Summary
In this article, we discussed ARM, a CPU architecture based on RISC principles, which emphasizes simplicity and efficiency. We explained how ARM differs from x86/CISC (Intel/AMD), noting that its smaller instruction set uses fewer transistors and less power. Additionally, we looked at how ARM has impacted Appleβs M-series chips, showing gains in performance, heat management, and battery life, along with the shift toward handling AI tasks on ARM hardware.
According to The Information (paywalled), Apple reportedly moved up the release of new Mac mini and Mac Studio models after unexpectedly strong enterprise demand for Macs capable of running AI workloads. MacRumors reports: Apple normally releases new Mac models in the autumn, closer to October or November, making this week's announcement unusually early, falling just before the anticipated arrival of new iPhone models. The Information says that the AI-driven boom in Mac Studio and Mac mini sales is behind the early launch. Apple noticeably promoted the ability to link multiple Mac Studios together into a single, more capable system for running large frontier AI models, a feature aimed at business and developer customers rather than everyday consumers.
Apple highlighted the Mac mini and Mac Studio's shift toward business buyers in June, with a "Business at the Park" event involving executives from major companies Ford, Disney, and Anthropic. The Mac mini was said to be the "darling" of the event. Even so, enterprise's rush toward powerful desktop Macs more broadly took Apple by surprise.
The company reportedly did not possess an engineering team dedicated to business customers or staff focused on developer relations, and lacked an enterprise AI strategy. Businesses that approached Apple asking to buy access to the company's Private Cloud Compute infrastructure were reportedly turned down. Apple is instead leaning on partners such as WebAI and Mount Thor, which provide AI tools and execution environments built on Apple hardware.
Apple has now released iOS 27 beta 8 IPSW links for download, with an OTA update also on offer if youβd rather go that route. That means that registered developers can download the update right now using their chosen method.
Recently [Tucker Osman] brought to life what once was just a meme created by the BBC as it poked fun at Appleβs push for ever smaller devices that also dropped many features along the path towards questionable innovation. This βMactiniβ was a super-small laptop, with just a single button that did everything, befitting the overpriced more-money-than-sense status symbol vibe that [Jony Ive] brought to Apple.
The input method suggested in the BBC video involves convoluted patterns using the single button, which would be functional, albeit cumbersome. Even as [Tucker] set out on this mission to make the Mactini real, he had to answer a lot of questions, while sticking to the rules that it had to follow the rough form factor of the original and have the same features.
In lieu of overcomplicating things, he opted to use a 1.69β³ ST7789V2-based SPI LCD along with a small RP2350-based board from Waveshare for the brains. Rather than just slapping the LCD example for the Pico board on it as firmware, he actually used DMA to do the screen data copying, freeing up a lot of CPU cycles.
The rest of the hardware was cobbled together from e-waste parts, including an iPad speaker and an audio amplifier from a scrap board. Video was handled using Motion JPEG decoding for a not-horrible-framerate. All of this was put into a 3D printed case that was designed in FreeCAD, to make the Mac that [Ive] could only have dreamed of back in the 2000s.
Apple has unveiled a new Mac mini with either its new M6 chip or the M5 Pro chip released earlier this year. It adds faster CPU, GPU, storage, and AI performance along with Wi-Fi 7, Bluetooth 6, and 2.5Gb Ethernet as standard. There's also a new higher starting price of $899, which is up from the previous $799 base price and far above the $599 starting price of the 2024 model. MacRumors reports: The M6 chip is equipped with a 12-core CPU and a 12-core GPU, up from a 10-core CPU and 10-core GPU in the M5 chip. In addition, the M6 chip features the first-ever dual Neural Engine with two 16-core engines, up from one in previous chips. The M6 chip has up to 170GB/s memory bandwidth, up from 153GB/s for the M5 chip.
Apple said the Mac mini with the M6 chip delivers up to 40% faster CPU performance, up to 4x faster performance for AI tasks in particular, up to 2x faster graphics performance, and up to 2x faster storage speeds compared to the previous-generation model with the 10-core M4 chip, 32GB of unified memory, and 2TB of storage.
There are two M5 Pro configurations available, including one with a 15-core CPU and a 16-core GPU, and another with an 18-core CPU and a 20-core GPU. Both of these models are equipped with a single 16-core Neural Engine for AI tasks. For both the M6 and M5 Pro configurations, Apple said the Mac mini now includes Neural Accelerators in each GPU core for the first time.
The Mac mini and Mac Studio occupy two distinct points in Apple's lineup of desktops, but lately, they've had something in common: They're popular for local AI inference and software development thanks to the advantages of their unified memory architecture and the fast CPUs and GPUs on their systems-on-a-chip.
Today, Apple announced new iterations of both desktops, along with two new chips: the M6, the first 2nm chip in Apple's M-series lineup for Macs, and the M5 Ultra, now the most powerful chip in the lineup for most thingsβespecially AI workloads.
This free, local AI dictation app pastes your words straight into any app, no subscription, no cloud required, and it's shockingly accurate. Here's why I can't stop using it.
Finder never bothered me until I tried Bloom. Multi-pane layouts, a floating Portal, and smarter search made me realize how much time I was quietly losing every day.