AI Inside: 6 Tasks You Can’t Do Well with a Regular CPU

“The world of computing is AI-driven.” Is this the correct statement to use in the current scenario? Well! A report suggests that Intel executives worldwide think that AI PCs will account for more than 50% of the overall PC market share by 2026.  From real-time data analysis to intelligent decision-making, AI is fueling the world of personal computing. 

Today, businesses and individuals want systems that can cope with their increasing work demands. While existing CPUs are good, they cannot match the efficiency and parallelism of what AI-optimized CPUs can achieve. These machines come with AI accelerators such as GPUs (Graphics Processing Units), NPUs (Neural Processing Units), and TPUs (Tensor Processing Units). These advanced technologies can perform operations with much more accuracy and reliability than existing CPUs can. 

So, let’s discuss some of the major tasks where AI-optimized processors can easily outperform your traditional CPU setup!

6 Tasks Where a Regular CPU Fails (And AI Hardware Excels)

1: Image and Video Recognition in Real-Time

Today, businesses mostly rely on visual data, whether it’s facial recognition or identifying objects and actions in images and videos. An AI computer comes with an intelligent processor that performs parallel processing of high scale to properly analyze videos and images in real time. 

• The CPU segments the process into small parallel batches. 
• Workloads such as Convolutional Neural Networks (CNNs) use three-dimensional data for image and video recognition. 
• Performs operations at extremely low latency

AI processors contain an endless number of cores that can process and enable real-time image and video recognition simultaneously. 

2:  Natural Language Processing (NLP)

Natural Language Processing has reformed the way human beings communicate with machines. NLP demands contextual relation processing, syntactic representations, and semantic meaning, including chat interface, language translation, content summary, etc.

• These processes are deeply related to deep learning models like BERT, GPT, and T5, where these models have billions of parameters and need large-scale tensor operations.
• The CPUs do not have the necessary bandwidth and processing throughput to handle the scale or complexity of NLP tasks. 

Chips used in AI processors are designed to perform better on AI tasks such as matrix multiplications, enabling the real-time process of understanding languages, analyzing contexts, and interpreting sentiments. Such functions are just not achievable under normal types of CPU systems.

3. Training in Machine Learning

Machine Learning tasks require forward propagation, error computation, and backpropagation. It is a compute-intensive cycle and involves repetitive mathematical calculations on large data.

• Conventional CPUs would not work speedily and efficiently in terms of this. They do not support the parallel tensor computations required to perform efficient training of models, particularly deep neural networks. 
• It will take days to train even a modest model using a CPU.

AI processing with accelerators like GPUs and TPUs enables the distributed computations, generous memory bandwidth, and swift floating-point operations, which are critical to the supervised and unsupervised training of models.

4. Edge AI On-Device Inference

Modern applications demand real-time response without the latency of having to use the cloud, and edge AI is becoming a necessity.  It helps devices like Smart cameras, industrial automation provide results with utmost accuracy and within a short period.

• Traditional CPUs are generally too power-hungry and slow to be put into neat edge devices. 
• They create unacceptable time lapses when there are time-sensitive operations.

AI accelerators, in particular, NPUs and custom SoCs, can be set to execute the inference locally with a high level of efficiency and low-power usage. This is what makes them very suitable to be implemented in constrained environments and real-time applications where latency is an issue.

5. Audio Processing and Speech Recognition

Speech recognition processes an audio signal in real time and attempts to analyze and interpret it to either process and translate a series of physical commands, transcribe a conversation, or respond with synthesized audio. Such tasks rely on learning models that include RNNs and attention-based transformers.

CPUs often struggle with time modeling and signal processing needed in voice-based interactions. They are inefficient at audio decoding and natural speech synthesis because of limited core count and a lack of dedicated AI instructions.

• AI chips can manage time-limited data more adequately, and also deliver multi-modal analysis, including merging audio data with text or video, or anything similarly.
• The result of this is higher speed and accurate voice recognition.

6. Generative AI Tasks

Generative AI is offering an advanced way of content creation. Just by entering the simple prompts, these can generate text, images, videos, and even code. These tasks are generally accomplished through large language models such as ChatGPT, Gemini, Microsoft Copilot, or others. According to a survey, more than 71% of the respondents say that they use Gen AI in at least one business function. 

• AI Accelerators use Gen AI to analyze billions of parameters to reduce time with the feasibility of real-time generation.
• Moreover, they also use parallelism and deep learning optimization to make content generation more feasible and accurate. 

Major Areas Where CPUs Fall Behind

Here are some of the main differences why AI-optimized processors can easily outperform CPUs at various levels. 

Performance Benchmarks: CPU vs GPU vs TPU

Conclusion 

AI is rewriting the rules of modern personal computing. PCs like an AI-driven computer come with advanced AI accelerators and chips that can perform various high-end tasks with utmost efficiency and speed as compared to traditional CPUs. From image and video recognition to generative AI tasks, AI processors can deploy intelligent applications faster, scale confidently, and stay ahead in an increasingly automated world.