Apple A16 Bionic vs Google Tensor G2
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CPU comparison with benchmarks
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| Apple A16 Bionic | Google Tensor G2 | |
CPU comparisonIn this CPU comparison, we compare the Apple A16 Bionic and the Google Tensor G2 and use benchmarks to check which processor is faster.
We compare the Apple A16 Bionic 6 core processor released in Q3/2022 with the Google Tensor G2 which has 8 CPU cores and was introduced in Q4/2022. |
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| Apple A series (22) | Family | Google Tensor (3) |
| Apple A16 (1) | CPU group | Google Tensor G2 (1) |
| 16 | Generation | 2 |
| A16 | Architecture | G2 |
| Mobile | Segment | Mobile |
| Apple A15 Bionic (5-GPU) | Predecessor | Google Tensor |
| Apple A17 Pro | Successor | -- |
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CPU Cores and Base FrequencyThe Apple A16 Bionic is a 6 core processor with a clock frequency of 3.46 GHz. The processor can compute 6 threads at the same time. The Google Tensor G2 clocks with 2.85 GHz, has 8 CPU cores and can calculate 8 threads in parallel. |
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| Apple A16 Bionic | Characteristic | Google Tensor G2 |
| 6 | Cores | 8 |
| 6 | Threads | 8 |
| hybrid (big.LITTLE) | Core architecture | hybrid (Prime / big.LITTLE) |
| No | Hyperthreading | No |
| No | Overclocking ? | No |
| 3.46 GHz 2x Everest |
A-Core | 2.85 GHz 2x Cortex-X1 |
| 2.02 GHz 4x Sawtooth |
B-Core | 2.35 GHz 2x Cortex-A78 |
| -- | C-Core | 1.80 GHz 4x Cortex-A55 |
Artificial Intelligence and Machine LearningProcessors with the support of artificial intelligence (AI) and machine learning (ML) can process many calculations, especially audio, image and video processing, much faster than classic processors. Algorithms for ML improve their performance the more data they have collected via software. ML tasks can be processed up to 10,000 times faster than with a classic processor. |
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| Apple A16 Bionic | Characteristic | Google Tensor G2 |
| Apple Neural Engine | AI hardware | Google Tensor AI |
| 16 Neural cores @ 17 TOPS | AI specifications | Google Edge TPU @ 4 TOPS |
Internal GraphicsGraphics (iGPU) integrated into the processor not only enable image output without having to rely on a dedicated graphics solution, but can also efficiently accelerate video playback. |
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| Apple A16 (5 GPU Cores) | GPU | ARM Mali-G710 MP7 |
| 1.34 GHz | GPU frequency | 0.90 GHz |
| -- | GPU (Turbo) | -- |
| 13 | GPU Generation | Vallhall 3 |
| 4 nm | Technology | 4 nm |
| 3 | Max. displays | 1 |
| 20 | Compute units | 7 |
| 640 | Shader | -- |
| No | Hardware Raytracing | No |
| No | Frame Generation | No |
| 6 GB | Max. GPU Memory | -- |
| -- | DirectX Version | 12 |
Hardware codec supportA photo or video codec that is accelerated in hardware can greatly accelerate the working speed of a processor and extend the battery life of notebooks or smartphones when playing videos. |
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| Apple A16 (5 GPU Cores) | GPU | ARM Mali-G710 MP7 |
| Decode / Encode | Codec h265 / HEVC (8 bit) | Decode / Encode |
| Decode / Encode | Codec h265 / HEVC (10 bit) | Decode / Encode |
| Decode / Encode | Codec h264 | Decode / Encode |
| Decode / Encode | Codec VP9 | Decode / Encode |
| Decode / Encode | Codec VP8 | Decode / Encode |
| No | Codec AV1 | Decode |
| Decode | Codec AVC | Decode / Encode |
| Decode | Codec VC-1 | Decode / Encode |
| Decode / Encode | Codec JPEG | Decode / Encode |
Memory & PCIeUp to 6 GB of memory in a maximum of 1 memory channels is supported by the Apple A16 Bionic, while the Google Tensor G2 supports a maximum of 12 GB of memory with a maximum memory bandwidth of 53.0 GB/s enabled. |
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| Apple A16 Bionic | Characteristic | Google Tensor G2 |
| LPDDR5-6400 | Memory | LPDDR5-5500 |
| 6 GB | Max. Memory | 12 GB |
| 1 (Single Channel) | Memory channels | 2 (Dual Channel) |
| 51.2 GB/s | Max. Bandwidth | 53.0 GB/s |
| No | ECC | No |
| 20.00 MB | L2 Cache | 8.00 MB |
| 24.00 MB | L3 Cache | 4.00 MB |
| -- | PCIe version | -- |
| -- | PCIe lanes | -- |
| -- | PCIe Bandwidth | -- |
Thermal ManagementThe Apple A16 Bionic has a TDP of 7.25 W. The TDP of the Google Tensor G2 is 10 W. System integrators use the TDP of the processor as a guide when dimensioning the cooling solution. |
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| Apple A16 Bionic | Characteristic | Google Tensor G2 |
| 7.25 W | TDP (PL1 / PBP) | 10 W |
| -- | TDP (PL2) | -- |
| -- | TDP up | -- |
| -- | TDP down | -- |
| -- | Tjunction max. | -- |
Technical detailsThe Apple A16 Bionic has 44.00 MB cache and is manufactured in 4 nm. The cache of Google Tensor G2 is at 12.00 MB. The processor is manufactured in 4 nm. |
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| Apple A16 Bionic | Characteristic | Google Tensor G2 |
| 4 nm | Technology | 4 nm |
| Chiplet | Chip design | Chiplet |
| Armv8.6-A (64 bit) | Instruction set (ISA) | Armv8-A (64 bit) |
| -- | ISA extensions | -- |
| -- | Socket | -- |
| None | Virtualization | None |
| No | AES-NI | No |
| iOS | Operating systems | Android |
| Q3/2022 | Release date | Q4/2022 |
| -- | Release price | -- |
| show more data | show more data | |
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8C 8T @ 2.85 GHz buy now on Amazon and save!
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Rate these processors
Average performance in benchmarks
⌀ Single core performance in 2 CPU benchmarks
⌀ Multi core performance in 3 CPU benchmarks
Geekbench 5, 64bit (Single-Core)
Geekbench 5 is a cross plattform benchmark that heavily uses the systems memory. A fast memory will push the result a lot. The single-core test only uses one CPU core, the amount of cores or hyperthreading ability doesn't count.
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Apple A16 Bionic
6C 6T @ 3.46 GHz |
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Google Tensor G2
8C 8T @ 2.85 GHz |
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Geekbench 5, 64bit (Multi-Core)
Geekbench 5 is a cross plattform benchmark that heavily uses the systems memory. A fast memory will push the result a lot. The multi-core test involves all CPU cores and taks a big advantage of hyperthreading.
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Apple A16 Bionic
6C 6T @ 3.46 GHz |
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Google Tensor G2
8C 8T @ 2.85 GHz |
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Geekbench 6 (Single-Core)
Geekbench 6 is a benchmark for modern computers, notebooks and smartphones. What is new is an optimized utilization of newer CPU architectures, e.g. based on the big.LITTLE concept and combining CPU cores of different sizes. The single-core benchmark only evaluates the performance of the fastest CPU core, the number of CPU cores in a processor is irrelevant here.
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Apple A16 Bionic
6C 6T @ 3.46 GHz |
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Google Tensor G2
8C 8T @ 2.85 GHz |
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Geekbench 6 (Multi-Core)
Geekbench 6 is a benchmark for modern computers, notebooks and smartphones. What is new is an optimized utilization of newer CPU architectures, e.g. based on the big.LITTLE concept and combining CPU cores of different sizes. The multi-core benchmark evaluates the performance of all of the processor's CPU cores. Virtual thread improvements such as AMD SMT or Intel's Hyper-Threading have a positive impact on the benchmark result.
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Apple A16 Bionic
6C 6T @ 3.46 GHz |
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Google Tensor G2
8C 8T @ 2.85 GHz |
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iGPU - FP32 Performance (Single-precision GFLOPS)
The theoretical computing performance of the internal graphics unit of the processor with simple accuracy (32 bit) in GFLOPS. GFLOPS indicates how many billion floating point operations the iGPU can perform per second.
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Apple A16 Bionic
Apple A16 (5 GPU Cores) @ 1.34 GHz |
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Google Tensor G2
ARM Mali-G710 MP7 @ 0.90 GHz |
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AnTuTu 9 Benchmark
The AnTuTu 9 benchmark is very well suited to measuring the performance of a smartphone. AnTuTu 9 is quite heavy on 3D graphics and can now also use the "Metal" graphics interface. In AnTuTu, memory and UX (user experience) are also tested by simulating browser and app usage. AnTuTu version 9 can compare any ARM CPU running on Android or iOS. Devices may not be directly comparable when benchmarked on different operating systems.
In the AnTuTu 9 benchmark, the single-core performance of a processor is only slightly weighted. The rating is made up of the multi-core performance of the processor, the speed of the working memory, and the performance of the internal graphics.
In the AnTuTu 9 benchmark, the single-core performance of a processor is only slightly weighted. The rating is made up of the multi-core performance of the processor, the speed of the working memory, and the performance of the internal graphics.
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Apple A16 Bionic
6C 6T @ 3.46 GHz |
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Google Tensor G2
8C 8T @ 2.85 GHz |
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Performance for Artificial Intelligence (AI) and Machine Learning (ML)
Processors with the support of artificial intelligence (AI) and machine learning (ML) can process many calculations, especially audio, image and video processing, much faster than classic processors. The performance is given in the number (trillions) of arithmetic operations per second (TOPS).
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Apple A16 Bionic
6C 6T @ 3.46 GHz |
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Google Tensor G2
8C 8T @ 2.85 GHz |
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Devices using this processor |
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| Apple A16 Bionic | Google Tensor G2 |
| Apple iPhone 14 Pro Apple iPhone 14 Pro Max |
Google Pixel 7 Google Pixel 7 Pro |
News and articles for the Apple A16 Bionic and the Google Tensor G2
MediaTek Dimensity 9000+ vs Apple A16 and Snapdragon 8+ Gen 1
Posted by Stefan on 2022-09-22
With the Dimensity 9000+, Mediatek presents the latest of its smartphone chips. The successor to the Mediatek Dimensity 9000 achieves higher CPU and GPU clock frequencies thanks to better binning. The CPU clock of the prime core (A core) increases from 3.0 to 3.2 GHz. And the ARM Mali-G710 MP10 GPU can now clock at 0.9 GHz (previously 0.85 GHz).
Both are a good improvement over the Dimensity 9000 in theory but the increase in performance should not usually be noticeable in practice. In the end, the overall performance of the Mediatek Dimensity 9000+ is impressive: Qualcomm Snapdragon 8+ Gen 1 is beaten in Geekbench 5 in both the single-core and multi-core benchmarks.
Both are a good improvement over the Dimensity 9000 in theory but the increase in performance should not usually be noticeable in practice. In the end, the overall performance of the Mediatek Dimensity 9000+ is impressive: Qualcomm Snapdragon 8+ Gen 1 is beaten in Geekbench 5 in both the single-core and multi-core benchmarks.
What we know about the new Apple A16 Bionic in Apples iPhone 14
Posted by Stefan on 2022-08-23
Every year Apple releases a new iPhone series and with the release of the series always the latest Apple A processor. This has become even more important in recent years, as it has also formed the basis for Apple's M processor series since 2020. The Apple M processors are based on the same core architecture as the Apple A processors, but have been significantly upgraded in many areas such as memory bandwith, amount of memory or cache.
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