Apple’s Transistor Budget

June 26, 2024 / Ben Bajarin

Apple’s M-series chips have revolutionized the Mac lineup, bringing a laser focus on performance and efficiency to the company’s laptops (portables), desktops, workstations, and even now cloud silicon. Thanks to the hard work of those dissecting Apple silicon X-ray images and highlighting blocks on the SoC, we can take a closer look at how Apple allocates its transistor budget across different components revealing insights into the company’s priorities and vision for the future of computing.


GPU Prioritization

Not just with M series silicon but A series as well, Apple has been continually spending more transistors on the GPU. A trend we believe will continue. That trend is shown as what percentage of the SoC has been given to the GPU over time.

– M1: ~25-30%
– M2: ~30-35%
– M3: ~35-40%
– M4: ~35-40% (including GPU MISC)

The priority of the GPU signals Apple’s commitment to the visual element of computing, which goes well beyond gaming although an increase in gaming graphical capability has clearly been a priority. Apple’s operating systems are highly differentiated from their competitors with visual/user-interface qualities and the increasing capabilities in graphics is a big way Apple will continue to differentiate their software platforms visually and enable new visual capabilities to third-party developers.

CPU: Balancing Performance and Efficiency

The CPU allocation shows a more nuanced evolution:

– M1: Performance ~15-20%, Efficiency ~10-15%
– M2: Similar to M1
– M3: Performance ~20-25%, Efficiency ~10-15%
– M4: Performance ~20-25%, Efficiency ~15-20%

Apple’s engineers continue to emphasize their relentless focus on performance per watt and we see that play out in their increasing of their transistor spend on efficiency cores. Apple does this in two ways however, their performance cores get more efficient and their efficiency cores get more performant.  And, at each new node jump, both of these performance and efficiency elements get even better.  While the M4 that was analyzed was for the iPad, when we see M4 for Mac, the same fundamental trend line will continue just increasing the number of performance and efficiency cores. With M4, Apple has adopted Arm-v9.2 which brings with it fundamental advancements in performance and efficiency cores as well, which should bring more than a modest upgrade in performance per watt to M4-based Macs.

Apple Neural Engine: Investing in an AI-Driven Future

The steady growth in transistor allocation to the Apple Neural Engine (ANE) is particularly telling:

– M1: ~5-10%
– M2: ~7-12%
– M3: ~10-15%
– M4: ~10-15%

It’s no secret we love NPUs. Apple’s Neural engine plays a key role in on-device AI capabilities and Apple has increased the amount of on-device AI processing (TOPS) with each new generation, they have remained steady with a 16-core NPU. Another interesting observation here, revealed by die-shot analysis, is it appears there are 8 core blocks for ANE which means each ANE core is a dual-core system of tensor/matrix processors. This consistent increase reflects Apple’s recognition of the growing importance of AI and machine learning in modern computing tasks. While Apple’s transistor spend has stayed steady between M3 to M4, we expect this to increase with M5.

When it comes to ML/AI acceleration, it is important to note ANE is not the only thing aiding in matrix operations. Apple’s adoption of Arm v9.2 includes CPU architecture improvements as well as SME/SVE extensions that will replace the AMX (Apple custom implementation) accelerator blocks that improve a range of calculations done on the P and E cores. Looking at M3 to M4, the dedicated blocks for these accelerators, are strategically placed by both the P and E cores taking up roughly 5-7% of the die area of M4.  Visual analysis suggests this is a slight increase over M3 and it will be very interesting to see if this increases in M5, although we argue giving more transistors to ANE makes more sense.

A Few Observations

  • Specialization is Key: The evolution of Apple’s transistor allocation reveals a clear trend towards more specialized processing units. This approach allows for optimized performance in specific tasks like AI, media processing, and graphics rendering.
  • Graphics as a Priority: The substantial and growing allocation to GPUs indicates that Apple sees graphics performance as a crucial differentiator in the competitive chip market.
  • Efficiency Doesn’t Mean Compromise: Despite the focus on performance, Apple’s increased allocation to efficiency cores in the M4 shows a commitment to battery life and thermal management while maintaining competitive performance.
  • AI Readiness: The consistent growth and stabilization of the Neural Engine’s allocation suggest Apple is positioning its chips to handle increasingly complex AI workloads.
  • Balanced Approach: While certain areas see significant growth, Apple maintains a balanced allocation, ensuring that improvements in one area don’t come at the expense of others.


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