This heralds a departure from conventional CPUs, indicating a trend towards specialised processing units meticulously optimised for the execution of AI models. Recent strides by Qualcomm and MediaTek underscore a broader industry progression wherein next-generation chipsets are evolving to integrate on-device generative AI capabilities.
So, what are AI chips, and how do they differ from traditional CPUs? Let us find out:
What are AI chips
AI chips represent a specialised category of semiconductors tailored to facilitate on-device AI capabilities, adept at executing Large Language Models (LLMs). These chips typically adopt a ‘system-on-chip’ (SoC) configuration, encompassing diverse functions beyond the central processing unit (CPU) responsible for general processing and computations.
How does AI work in practice
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Why dedicated AI chips
On-device interpretation necessitates specialised processing segments, since conventional CPUs are good only at serial processing – one process at a time. For devices to process AI tasks on-device, it is essential for such devices to have a dedicated chip that can execute multiple calculations and processes simultaneously. Graphic Processing Units (GPUs), for example, are capable of accomplishing such workloads, but these are not designed specifically for AI tasks. Therefore, a forked version of GPUs or a dedicated AI chip is needed.
How are AI chips different from CPU
Both CPUs and AI chips achieve heightened computations per unit of energy by integrating numerous smaller transistors that operate at a faster pace and consume less energy than their larger counterparts. However, AI chips, unlike general-purpose CPUs, incorporate design features optimised for distinct processing methodologies.
CPUs employ a sequential computing method, issuing one instruction at a time, with subsequent instructions awaiting the completion of their predecessors. In contrast, AI chips diverge from CPUs, harnessing parallel computing to simultaneously execute numerous calculations. This parallel computing approach results in swifter and more efficient processing.
Various types of AI chips cater to diverse purposes. GPUs are primarily utilised in the initial development and refinement of AI algorithms, while Field Programmable Gate Arrays (FPGAs) apply trained AI algorithms to real-world data inputs. Application-specific integrated circuits, characterised by design flexibility, can be employed for both training and inference tasks.
Due to their distinctive attributes, AI chips exhibit a notable advantage in speed and efficiency over CPUs, contributing to the enhanced training and inference of AI algorithms.
