Google Processor – Tensor

The most widely used search engine on the planet, Google, is a technological titan that has revolutionized how we access information. Google has now grown into a leading worldwide technology company that has significantly influenced the digital environment.

Google is credited with revolutionizing web search; when the internet started taking shape, Yahoo and Excite were beaten by Google’s straightforward design. As a result, Google is responsible for over 80% of all web search traffic.

Google stands apart from other digital behemoths due to its user-centric philosophy and unwavering focus on innovation. The company’s goal has always been to organize the world’s information and benefit everyone.

Lets see the in-depth description regarding Google processor:
Google has recently made headlines by announcing the Tensor Processing Unit (TPU) and its successor, the TPUv2. These custom-built chips are designed specifically for accelerating machine learning workloads and have enabled Google to achieve impressive performance gains in its own applications.

The original TPU was announced in 2016 and was designed to accelerate the inference stage of machine learning workloads, which is where a trained model is used to make predictions on new data.

In addition, the TPU was designed to be highly efficient, delivering up to 30 times higher performance per watt than traditional CPUs and GPUs. This efficiency allowed Google to improve its applications’ responsiveness while reducing the energy required to run them.

In 2017, Google announced the TPUv2, designed to accelerate training and inference workloads. The TPUv2 is built on a custom chip architecture that delivers even higher performance than the original TPU. For example, the TPUv2 can perform up to 180 trillion floating point operations per second (TFLOPS), more than twice the original TPU.

One of the key benefits of the TPUs is that they are integrated into Google’s cloud platform, allowing developers to access this powerful hardware for their own applications easily. This has enabled developers to build and deploy machine learning models at scale without investing in expensive hardware.

Google’s investment in custom-built hardware for machine learning has set it apart from other cloud providers and has enabled it to deliver innovative applications and services to its users. With the release of the TPUv3 in 2019 and the TPUv4 in 2021, it’s clear that Google is committed to continuing its investment in custom hardware for machine learning, and we can expect to see even more impressive performance gains in the future.

In 2023,

Tensor G2 processor

It’s expected to utilize the Tensor G2 processor but will have camera upgrades, including the 64-megapixel Sony IMX787 main sensor.

Compare Google Processor By Specs

  • Google Tensor G3 Specs

    Google Tensor G3 Specs

    • Processor(CPU): Upcoming | ARM Cortex
    • Cores: Octa core (8)
    • Graphics(GPU): Upcoming | ARM Mali
    • Display: Full HD | QHD+
    • RAM: 12GB | Upcoming
    • Camera: Upcoming
  • Google Tensor G2 Specs

    Google Tensor G2 Specs

    • Processor(CPU): 2x 2.85GHz - ARM Cortex-X1 2x 2.35GHz - ARM Cortex A78 4x 1.80GHz - ARM Cortex A55
    • Cores: Octa Core (8)
    • Graphics(GPU): ARM Mali-G710 MP7
    • Display: Full HD
    • RAM: LPDDR5
    • Camera: Dual Camera 12MP + 48MP | Single Camera 50MP
  • Google Tensor Specs

    Google Tensor Specs

    • Processor(CPU): 2x 2.8GHz - ARM Cortex-X1 2x 2.25GHz - ARM Cortex A76 4x 1.8GHz - ARM Cortex A55
    • Cores: octa core(8)
    • Graphics(GPU): Mali-G78 MP20
    • Display: Full HD
    • RAM: LPDDR5
    • Camera: Dual Camera 2x 48MP | Single Camera 240MP