Artificial intelligence learning and reasoning
Artificial intelligence learning includes the use of large datasets to train machines to establish regular models, and then verify and improve it through artificially marked data to improve accuracy. In this process, artificial intelligence chips support the calculation needs of parameter modification and model optimization. On the contrary, artificial intelligence reasoning involves the application of the new sample or identification of the new samples. This requires an artificial intelligence chip with high computing power to perform fast reasoning, such as real -time object detection in autonomous cars.
Artificial intelligence chip type and performance
Although the CPU, GPU, and FPGA can perform artificial intelligence model calculations, their chip architecture affects performance. In artificial intelligence chips, special integrated circuits (ASICs) are designed for artificial intelligence computing. However, due to its parallel processing capabilities and programming, the GPU acceleration system occupies a dominant position in the artificial intelligence workload of the data center, although its power consumption is high.
Cloud and edge artificial intelligence chips demand
Clouds and edge/terminal applications have different demand for artificial intelligence chips. Cloud learning and reasoning require high -performance chips such as GPU and ASIC, and edge and terminal devices usually use low -power AI chips optimized for specific tasks. Major companies have formulated different strategies for cloud, edge, and terminal artificial intelligence chip products to meet different application needs.
Application of artificial intelligence chips
Artificial intelligence chips are widely used in various fields, including automobiles, medical care and smart home systems. In the automotive field, artificial intelligence chips can improve driver safety and in -vehicle experience through environmental perception, vehicle positioning, driver behavior monitoring, and smart cockpit assistant.
In the field of medical care, artificial intelligence chips can realize applications such as motion analysis, brain interface and virtual training, thereby improving medical care management, sports performance and medical care.
In addition, the artificial intelligence chip that integrates voice and image recognition functions is being integrated into a single -chip solution in the smart home system to achieve functions such as voice command analysis, noise elimination, and facial recognition.
Artificial intelligence chip packaging trend
With the increase of the function and complexity of the artificial intelligence chip, the requirements for the density of packaging interconnection and electrode distance have also increased, which has promoted the development of the development of traditional welding lines to pour chips and advanced 2.5D/3D packaging technology. High -performance calculation (HPC), 5G, and artificial intelligence applications require higher transistor density, and it is difficult to achieve only process expansion. Advanced packaging technology provides alternatives by reducing the spacing of chip and achieving three -dimensional stacks, thereby reducing power consumption while improving the calculation density and performance.
Realize 2.5D/3D packaging of high integratedness
Compared with the traditional two -dimensional system -level packaging (SIP) solution, 2.5D packaging has achieved closer chip integration through the inner plug -in and convex blocks. Three -dimensional packaging further improves the integrated density through vertical stacking chips, which can accommodate more functional chips and reduce signal transmission distance and power consumption.
Leading semiconductor manufacturers and packaging factories are actively investing in advanced packaging technologies because they recognize the potential of these technologies as the key driving force for the development of Moore's law semiconductor development.
Data center artificial intelligence chip packaging high -end GPUs for data center artificial intelligence workloads are usually packaged with integrated high -bandwidth memory (HBM) substrate (COWOS) to achieve high computing performance and memory bandwidth. HBM is a stacked DRAM chip that can achieve high data transmission rates through silicon pores and high -density interconnected electrodes.
Edge artificial intelligence chip packaging
For marginal artificial intelligence applications, chip manufacturers such as Winbond and Huahong Semiconductor have developed solutions for stacking DRAM chips stacked on the processing chip. Compared with SRAM -based solutions, this method can not only expand the capacity of high -speed buffer downturn, but also ensure higher data access speed, thereby providing mid -range computing capabilities at a lower cost.
Future trend: vertical crystal stacking
The Law of Moore promoted the expansion of the transistor to improve performance, but this law is close to the limit. International devices and system roadmaps (IRDS) show that vertical transistor stacking will be the future development direction. From 2028, the transistor may transition from the FINFET and the GAA structure to the complementary FET (CFET) and three -dimensional ultra -large -scale integrated circuits. This requires an advanced three -dimensional packaging technology in order Vertical stacking and interconnection.
The introduction of the GAA transistor has challenged in terms of high vertical and horizontal comparison and vertical channel formation. In order to achieve the CFET channel stacking, solutions such as wafer bonding and layer transfer technology have been proposed.
With the design steering the vertical transistor stacking and separation signal and the power distribution network, and the advanced packaging technology such as nano -class silicon pores (TSV) is important for connecting the power rail from the back of the chip to the transistor layer.
Driven by the long -term demand of automotive electronics, data centers, and wireless communication industries, the semiconductor industry is expected to achieve positive growth in 2024. Artificial intelligence chips supporting artificial intelligence learning and inference calculations are an important hardware foundation for artificial intelligence IoT applications. Advanced packaging technology plays an important role in achieving the functional integration, performance improvement and transistor stack of artificial intelligence chips.
The packaging company should focus on the development of a flexible configuration modular artificial intelligence chip solution to meet the customized specifications while maintaining cost -effectiveness. Based on cost considerations, integrate chips to circuit boards or use advanced packaging to achieve chip integration, which can provide convenient and compact solutions respectively.
With the development of the next -generation high -performance computing chip to the vertical transistor stacking and back power distribution network, the advanced packaging technology that can achieve precise three -dimensional chip stacking and interconnect can become more and more important at 10 nanometer -scale. Bring opportunities and challenges.
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