How does an integrated chip support different models such as Transformer?

Memory and computing integrated chip is a kind of integrated storage and computing functions of EP1S30F1020I6 chip, it integrates storage and computing units on the same chip, can provide efficient storage and computing power, so as to support a variety of different models, including Transformer.

Transformer is a neural network model for natural language processing tasks, which has a large number of parameters and complex computational operations. In traditional computer systems, storage and computation are separated and data needs to be loaded from a storage unit (such as memory) to a computing unit (such as CPU) for computation. This separation of data transmission and computation results in high latency and power consumption. By integrating the storage and computing functions on the same chip, the local calculation of data can be realized, the cost of data transmission can be reduced, and the computing efficiency can be improved.

The key to supporting different models such as Transformer is its integrated storage and computing unit. memory and computing integrated chips usually use Non-volatile Memory (NVM) as a storage unit, such as Phase Change Memory (PCM), Magnetic Random Access Memory (MRAM), etc. These memories have fast read and write speeds and high density to meet the large-scale parameter requirements of models such as Transformer.

However, due to its large number of parameters and computing requirements, the Transformer model has high requirements on hardware resources, which requires certain features of the memory and computing chip to support. Here's how the on-board chip supports key features of different models such as Transformer:

1. Powerful computing power: Transformer model has a large number of parameters and complex calculation diagram structure, which requires a large amount of floating point computing power. Memory and computing chips require high-performance computing units, such as dedicated Vector Processing units and Matrix Multiplication accelerators, to support critical operations such as matrix arithmetic.

2, high-bandwidth storage system: Transformer model needs to frequently read and write parameters and intermediate results, so the Memory and computing chip needs to have a high-bandwidth storage system, such as cache and On-chip memory, to reduce data transmission delay and improve data access efficiency.

3, flexible memory management: the parameters of the Transformer model are usually large, which may exceed the storage capacity of the integrated chip. Therefore, the integrated memory chip needs to have a flexible memory management mechanism to support data exchange and migration between the chip and the external memory to meet the storage requirements of the model parameters.

4. Efficient parallel calculation: The Transformer model is highly parallel and can handle the calculation of multiple input samples or multiple locations at the same time. Memory and computing chips need parallel computing capabilities, such as multi-core processors and hardware thread schedulers, to achieve efficient computational concurrency and speed up model training and inference processes.

5, low power consumption and high energy efficiency: storage and computing integrated chips are usually used in mobile devices and embedded systems, which have high requirements for power consumption and energy efficiency. In order to support large models such as Transformer, memory and computing chips need to adopt low-power design and optimization algorithms to improve energy efficiency and extend the device's battery life.

In addition to the storage unit, the storage and computing chip also integrates a computing unit. Computing units are typically based on heterogeneous computing architectures, including traditional cpus, Gpus, and dedicated accelerators such as Tensor Processing Units (TPU), Field Programmable Gate Array (FPGA), and so on. These units can support different computing operations, such as matrix multiplication, convolution, etc., to meet the computing power needs of models such as Transformer.

The on-board chip can also further optimize the computing performance of models such as Transformer with a customized hardware architecture and instruction set. For example, some common operations in Transformer can be hardwareized to reduce computing latency and improve computing efficiency. At the same time, specialized instruction sets can be designed to support the computational needs of models such as Transformer, providing a higher level of abstraction and simplifying the programming and optimization process.

In short, the integrated storage and computing chip provides efficient storage and computing capabilities by integrating storage and computing functions, supporting the computing needs of different models such as Transformer. The customized hardware architecture and instruction set can further optimize the computing performance and improve the computing efficiency. The development of memory and computing chips will promote technological progress in fields such as artificial intelligence and natural language processing.