10 Chips for AI In 2022 (Updated)

10 Chips for AI In 2022 (Updated)

10 chips for AI

These Chips can enable deep learning applications on phones and other edge computing gadgets.

Here is a list of top innovative chips which have changed the AI world.

1. Intel’s Nervana

Intel of late revealed new details of upcoming high-performance AI accelerators: Intel Nervana, neural network processors. It is built to emphasize two primary real-world considerations: training a network very quickly and able to do in a particular power budget.

This processor is built with flexibility and maintains a perfect balance between performance, computing, and memory.

2. AMD Radeon Instinct

It is a Superior Training Accelerator for machine intelligence and deep learning
Developed with sophisticated “VEGA” graphics architecture made to handle large data sets and varied compute workloads.

Capable of 24.6 TFLOPS of FP16 highest compute performance for deep learning applications.

3. Samsung Exynos 9

Samsung’s Exynos 9820 has a distinct hardware AI-accelerator, or NPU, which does AI-related jobs almost seven times faster than it’s predecessor.

It is targeted at AI-related processing that will be done directly on the gadget instead of moving the task to a server, giving a faster performance and better security.

4. Nokia Reefshark

Reefshark – A unique and new chipset that helps in easing 5G network roll-out. AI is implemented in the design of the chip for radio and embedded in the baseband to use augmented deep learning to trigger smart, the swift response by the autonomous, cognitive network, which will help in enhancing network optimization and expanding business opportunities.

5. Apple A13 Bionic

This processor by Apple is used in it’s iPhone 11. Apple says that it’s the fastest processor by the company.
This chip features an Apple-designed 64-bit ARMv8.3- CPU with six-cores, where two high-performance cores are running at 2.65 GHz. The 2 supercharged cores are 20% faster, with a 30% reduction in power utilization, the four highly efficient cores are 20% faster with a 40% reduction in power usage.

6. Google Edge TPU

This chip by Google is purpose-built to run AI at the Edge. It delivers great performance in a small physical and power footprint, allowing the placement of highly precise AI at the edge.

Edge TPU merges custom hardware, open software, futuristic AI algorithms to give high-quality, easy to position AI solutions for the edge.

7. Graphcore GPU

Graphcore’s Intelligence Processing Unit is fully different from GPU and CPU’s, which are available today.

It is a very flexible, handy, parallel processor that has been designed thoroughly to deliver advanced performance on present machine intelligence models for both reasoning and training.

8. Cerebras Wafer Scale Engine

Manufacturers are racing towards making thinner, smaller, and affordable chips, but here, Cerebras has released a wafer-scale engine, 215mm x 215mm chip targeted towards deep learning applications.
It has 1.2 trillion transistors, packed onto a single side with 400,000 AI-optimised cores, connected by a 100Pbit/s interconnect. These cores are supplied with 18 GB of super-fast, on-chip memory, with an unrivaled 9 PB/s of memory bandwidth.

9. Huawei Ascend 910

Ascend 910 is a new AI processor that is part of the Huawei’s series of Ascend-Max chipset group. After a long time of continuous development, test results now show that the Ascend processor delivers on its planned performance goals with very low power usage than initially stated and arranged.

10. Alibaba Pingtouge Hanguang

Alibaba made public its first AI dedicated processor (cloud-based) for large-scale AI inferencing.
This 12-nm Hanguang 800 consists of 17 billion transistors, and it is 15 times more potent than the NVIDIA T4 GPU, and nearly 46 times more potent than the NVIDIA P4 GPU.


The uses of AI are already everywhere around today. From a general consumer to big business applications. With the extraordinary rise of connected gadgets, combined with an interest in privacy, low latency, and bandwidth limitations, the hardware equipment utilized for preparing AI models needs to have that additional edge.


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