Optimization of Language Models for Efficient Inference and Performance Using Mixed Architectures

Author(s): Antonello Sale

Originally published on Towards AI.

The world of artificial intelligence is changing rapidly, right? One of the pillars of this transformation has been the adoption of large language models(LLM) and we cannot imagine the development of AI without them. From GPT-3 to BERT, these models are revolutionizing natural language processing, developing machines that understand, generate, and interact in human languages. Yet these large models often result in huge computational demands, and their applications in practice are mostly difficult under great pressure for efficiency and performance.

Making language models more efficient for inference is not only a technical necessity; it is a path toward broader accessibility and practical deployment of these capabilities. This should be considered as involving a reduction in latency and minimization in resource consumption while maintaining or even enhancing the performance of the model. This would mean focusing on major techniques such as quantization, pruning, and knowledge distillation to reduce model size radically and also improve inference time without compromising in result quality.

Further optimizations come from innovative architecture designs and hardware advancement. In combination with other efficient architectures, like MobileBERT, and optimization schemes, it is by dint of hardware acceleration that full utilization of the power of GPUs and TPUs extends these models to their upper limits in real-time scenarios. With this approach, the parallel computing framework for handling the data becomes sophisticated, and the inference procedure also moves smoothly; very effective distribution of computation becomes possible in this way. The way to optimize a language model is to walk a tightrope in keeping the very fragile balance between speed and accuracy. This approach will unlock the potential of language models in performing complex tasks swiftly with fewer resources, thus being practical in a wider range of applications. Whether we want to optimize the user experience from real-time applications or power intelligent systems, the potential gains delivered from optimized language models are huge and manifold. This article will get into the methodologies and techniques that power these improvements and provide a vision of what lies ahead for high-performance, efficient language models.

Quantization: Enhancing Efficiency Through Reduced Precision

Let’s start the process of optimizing the performance and efficiency of a language model with quantization. This is a powerful technique in the realm of machine learning and deep learning aimed at optimizing language models for efficient inference. By reducing the precision of model weights, quantization effectively decreases memory usage and speeds up computation, all while maintaining a high level of accuracy. This process involves converting the 32-bit floating-point weights typically used in deep learning models to lower precision formats such as 16-bit or 8-bit integers. Here’s a detailed look at how quantization works and its benefits:

How Quantization Works

1. Precision Reduction:

• 32-bit to 16-bit: The first step often involves converting 32-bit floating-point weights to 16-bit floating-point. This is known as half-precision floating-point (FP16). The primary advantage is that it reduces the memory footprint by half and can double the speed of computation due to reduced data movement and improved cache utilization.
• 32-bit to 8-bit: For even more aggressive optimization, weights can be further reduced to 8-bit integers. This requires more sophisticated techniques to ensure that the lower precision does not degrade the model’s performance significantly.

2. Static vs. Dynamic Quantization:

• Static Quantization: This involves quantizing the weights and activations during training. The model learns to handle lower precision data, resulting in a robust performance during inference.
• Dynamic Quantization: In this method, weights are quantized post-training, typically during inference. Activations are quantized dynamically at runtime, offering a balance between model size and inference speed without the need for retraining.

3. Quantization-Aware Training (QAT):

• This advanced technique integrates quantization into the training process. By simulating lower precision during training, the model adapts to the precision constraints, leading to higher accuracy post-quantization compared to models quantized after training.

Benefits of Quantization

1. Reduced Memory Usage:

• Lower precision weights consume less memory, which is particularly beneficial for deploying models on devices with limited resources, such as mobile phones and IoT devices.

2. Increased Computation Speed:

• Reduced precision allows for faster arithmetic operations. This speedup is especially significant on specialized hardware like GPUs and TPUs, which are optimized for lower-precision calculations.

3. Improved Energy Efficiency:

• Quantized models consume less power, which is crucial for battery-operated devices and large-scale data centers aiming to reduce energy costs.

4. Maintained Accuracy:

• With proper techniques like quantization-aware training, models can achieve almost the same accuracy as their higher-precision counterparts. The trade-off between precision and accuracy is minimal, making quantization an attractive optimization method.

Challenges and Considerations

1. Maintaining Model Accuracy:

• While quantization offers significant benefits, ensuring that the reduced precision does not negatively impact the model’s performance is a challenge. Careful tuning and techniques like quantization-aware training help mitigate this issue.

2. Hardware Support:

• The effectiveness of quantization largely depends on hardware support. Modern processors, GPUs, and TPUs are increasingly designed to handle lower precision computations, but older hardware may not offer the same level of support.

3. Framework Compatibility:

• Ensuring that machine learning frameworks (like TensorFlow, PyTorch, etc.) and libraries fully support quantization and provide the necessary tools for its implementation is critical for seamless integration into the development pipeline.

Quantization stands out as a vital technique in optimizing language models for efficient inference. By intelligently reducing precision, it strikes a balance between performance and resource utilization, making it an essential tool for deploying advanced AI models in resource-constrained environments.

Pruning: Streamlining Language Models for Enhanced Efficiency

Have you ever heard of pruning? Pruning is another technique used to optimize language models by removing redundant or less important neurons and layers. This reduction in model complexity decreases both the size and inference time of the model while striving to maintain most of its original performance. Pruning is essential for making large models more efficient, enabling their deployment in environments with limited computational resources. Here’s a detailed exploration of how pruning works and its benefits:

How Pruning Works

1. Identifying Redundant Neurons and Connections:

• Weight Magnitude Pruning: This method involves ranking the weights by their absolute values and removing those with the smallest magnitudes. The assumption is that weights with smaller values contribute less to the overall model output and can be pruned without significant loss in performance.
• Activation-Based Pruning: This technique prunes neurons that have the least activation (i.e., the least contribution to the output) across various inputs. Neurons that are rarely activated can be considered redundant.

2. Structured vs. Unstructured Pruning:

• Structured Pruning: This approach removes entire neurons, filters, or channels, thereby maintaining the structured integrity of the neural network. Structured pruning is more hardware-friendly and easier to implement, as it leads to more regular sparsity patterns.
• Unstructured Pruning: This method removes individual weights, resulting in irregular sparsity patterns. While it can lead to higher sparsity and potentially greater reductions in model size, it is more challenging to achieve significant speedups during inference due to the irregularity.

3. Iterative Pruning and Fine-Tuning:

• Iterative Pruning: Pruning is often done iteratively, with small portions of the network being pruned at each step. After each pruning step, the model is retrained (fine-tuned) to recover from any loss in performance.
• Fine-Tuning: Post-pruning, the model undergoes fine-tuning to adjust the remaining weights and compensate for the loss of the pruned elements. This helps in restoring the model’s performance close to its original state.

Benefits of Pruning

1. Reduced Model Size:

• By removing unnecessary parameters, pruning significantly reduces the size of the model. This makes it more feasible to deploy on devices with limited storage capacity, such as mobile phones and edge devices.

2. Faster Inference:

• A smaller model size translates to fewer computations during inference, leading to reduced latency and faster response times. This is particularly beneficial for real-time applications where quick decision-making is crucial.

3. Lower Memory and Energy Consumption:

• With fewer parameters to store and process, pruned models consume less memory and require less energy. This efficiency is critical for battery-powered devices and data centers aiming to cut down on operational costs.

4. Maintained Performance:

• Effective pruning strategies ensure that the reduction in model size does not come at the expense of significant accuracy loss. Techniques like iterative pruning and fine-tuning help in maintaining a balance between efficiency and performance.

Challenges and Considerations

1. Determining Pruning Criteria:

• Identifying which neurons or connections to prune without adversely affecting model performance is a complex task. Various criteria and heuristics can be employed, but finding the optimal approach often requires experimentation and domain knowledge.

2. Balancing Sparsity and Speedup:

• While pruning can introduce sparsity, achieving actual speedup during inference depends on the hardware and software support for sparse computations. Structured pruning tends to offer more predictable speedups compared to unstructured pruning.

3. Maintaining Robustness:

• Excessive pruning or incorrect pruning criteria can lead to a significant drop in model performance. Careful calibration of the pruning process and thorough testing are essential to ensure the robustness of the pruned model.

4. Framework and Hardware Compatibility:

• Ensuring compatibility with machine learning frameworks and leveraging hardware acceleration for sparse models are crucial for realizing the benefits of pruning. Support for pruning varies across frameworks and hardware, necessitating careful selection and configuration.

Pruning is a vital optimization technique that effectively reduces the size and complexity of language models, enhancing their efficiency and making them more suitable for deployment in resource-constrained environments. By selectively removing less important neurons and connections, pruning strikes a balance between performance and computational efficiency, paving the way for more practical and scalable AI applications.

Knowledge Distillation: Teaching Smaller Models to Perform Efficiently

Ok, now let’s talk about knowledge distillation. This is an advanced technique used to optimize language models by training a smaller model, referred to as the “student,” using the outputs of a larger, well-performing model, known as the “teacher.” This approach allows the student model to achieve performance levels comparable to the teacher model but with significantly lower computational cost and resource requirements. Here’s an in-depth look at how knowledge distillation works and its benefits:

How Knowledge Distillation Works

1. Teacher Model Training:

• The first step involves training a large, complex model (the teacher) on the target dataset. The teacher model is usually a high-capacity network that achieves state-of-the-art performance but is resource-intensive.

2. Soft Targets Extraction:

• Once the teacher model is trained, it generates outputs for the training data. These outputs, known as “soft targets” or “soft labels,” include the predicted probabilities for each class. Unlike hard labels (ground truth), soft targets provide more information about the teacher’s confidence and the relative probabilities across classes.

3. Student Model Training:

• The student model, typically smaller and more efficient, is trained using both the hard labels and the soft targets from the teacher model. The loss function for the student model incorporates both the standard cross-entropy loss with the hard labels and an additional loss term that minimizes the difference between the student’s and teacher’s soft targets.

4. Temperature Scaling:

• During the distillation process, temperature scaling is applied to the soft targets to smooth the probability distribution. A higher temperature value softens the probabilities, providing more nuanced information about the teacher model’s predictions. The same temperature is used during the student model’s training to match this softened output.

Benefits of Knowledge Distillation

1. Model Compression:

• Knowledge distillation allows for compressing large models into smaller ones without substantial loss in performance. The student model, being less complex, requires fewer parameters and less memory.

2. Enhanced Efficiency:

• The student model, being smaller, performs inference faster and consumes less computational power. This efficiency is critical for deploying models in resource-constrained environments, such as mobile devices or edge computing scenarios.

3. Transfer of Generalization Capabilities:

• The soft targets from the teacher model carry more information than hard labels alone, including the relative likelihoods of incorrect classes. This additional information helps the student model learn better generalization capabilities, often leading to improved performance on unseen data.

4. Simplified Training:

• Training a smaller student model from scratch using standard methods might require extensive tuning and experimentation. Knowledge distillation simplifies this process by leveraging the well-tuned teacher model’s outputs.

Challenges and Considerations

1. Quality of Teacher Model:

• The effectiveness of knowledge distillation heavily depends on the performance of the teacher model. A poorly performing teacher will transfer inadequate knowledge, leading to a suboptimal student model.

2. Balancing Loss Terms:

• Properly balancing the cross-entropy loss with hard labels and the distillation loss with soft targets is crucial. This balance ensures that the student model learns effectively from both the teacher’s knowledge and the ground truth.

3. Temperature Selection:

• The choice of temperature during the distillation process affects the soft target distribution. Finding the right temperature value is essential for effectively transferring knowledge from the teacher to the student model.

4. Student Model Architecture:

• Designing an appropriate student model architecture is important. It should be small enough to benefit from the efficiency gains but sufficiently powerful to learn from the teacher model’s distilled knowledge.

Applications and Impact

1. Resource-Constrained Deployment:

• Knowledge distillation enables deploying high-performing models in environments with limited computational resources, such as mobile devices, IoT devices, and real-time applications.

2. Model Scalability:

• It allows scaling down large models to meet specific requirements without substantial loss in accuracy, making AI more accessible and practical across various industries.

3. Enhanced Training Efficiency:

• By leveraging the distilled knowledge, training smaller models becomes more efficient and requires less computational overhead compared to training large models from scratch.

As we have seen, knowledge distillation stands out as a transformative technique in the optimization of language models. By effectively transferring knowledge from a large, well-performing teacher model to a smaller, more efficient student model, it achieves a balance between high performance and computational efficiency. This method not only makes advanced AI models more practical for real-world applications but also opens up new possibilities for deploying AI in diverse and resource-limited environments.

Model Compression: Techniques for Reducing Model Size and Enhancing Inference Speed

Model compression encompasses a variety of techniques aimed at reducing the size of language models and improving their inference speed. By making models more compact, compression techniques help in deploying AI applications on devices with limited computational resources while maintaining a high level of performance. Here’s an in-depth look at some common model compression techniques, including weight sharing, matrix decomposition, and sparse representations.

Techniques for Model Compression

1. Weight Sharing:

• Concept: Weight sharing involves grouping similar weights in the model and sharing a single value among them. Instead of each weight having its unique value, weights within a group share a common value.
• Implementation: A typical approach is to cluster the weights into groups based on their values and assign the average value of each cluster to the weights in that group. During inference, a lookup table is used to replace each weight with its shared value.
• Benefits: This significantly reduces the number of unique parameters in the model, leading to lower memory usage and faster inference due to reduced computational requirements.

2. Matrix Decomposition:

• Concept: Matrix decomposition techniques factorize large matrices (such as weight matrices in neural networks) into products of smaller matrices. Common methods include Singular Value Decomposition (SVD) and low-rank approximations.
• Benefits: This reduces the number of parameters and computational complexity. The model retains most of its representational power while requiring fewer resources during inference.

3. Sparse Representations:

• Concept: Sparse representations involve making the model’s weight matrices sparse, meaning that many of the weights are zero. Sparse models require less memory and computational power because operations involving zero weights can be skipped.
• Implementation: Sparsity can be induced through techniques such as pruning (removing small-magnitude weights), regularization (adding a sparsity-inducing term to the loss function), and training methods designed to encourage sparsity.
• Benefits: Sparse models are lighter and faster. They can exploit specialized hardware and libraries optimized for sparse operations, further enhancing inference speed.

Benefits of Model Compression

1. Reduced Model Size:

Compressed models require less storage space, making them suitable for deployment on devices with limited memory, such as mobile phones and embedded systems.

2. Faster Inference:

• Smaller models with fewer parameters lead to quicker computations and lower latency during inference, which is crucial for real-time applications.

3. Lower Energy Consumption:

• With reduced computational requirements, compressed models consume less power, extending battery life for portable devices and reducing energy costs in large-scale deployments.

4. Maintained Performance:

• Effective compression techniques ensure that the reduction in model size and complexity does not come at a significant loss in performance. This balance is essential for practical applications.

Challenges and Considerations

1. Trade-Off Between Compression and Accuracy:

• Compressing a model too aggressively can lead to a loss in accuracy. Finding the right balance between reducing model size and maintaining performance requires careful tuning and validation.

2. Implementation Complexity:

• Some compression techniques, such as matrix decomposition and inducing sparsity, can be complex to implement and require a deep understanding of the underlying mathematics and model architecture.

3. Hardware and Software Support:

• The benefits of model compression are maximized when there is adequate support from hardware and software. Specialized libraries and hardware accelerators optimized for sparse computations can significantly enhance the efficiency of compressed models.

4. Compatibility with Training Pipelines:

• Integrating compression techniques into existing training pipelines can be challenging. It may require modifications to the training algorithms and additional computational overhead during the training phase.

Applications and Impact

1. Mobile and Edge Computing:

• Model compression is particularly beneficial for deploying AI models on mobile devices and edge computing environments, where computational resources are limited.

2. Cloud Services:

• In cloud-based AI services, compressed models reduce the cost of storage and computational resources, leading to more efficient and cost-effective solutions.

3. Real-Time Applications:

• Faster inference times enabled by model compression make it feasible to deploy AI in real-time applications such as augmented reality, autonomous driving, and interactive virtual assistants.

4. Environmental Impact:

• By reducing energy consumption, model compression contributes to the sustainability of AI technologies, helping to minimize their environmental footprint.

Model compression is a crucial technique in the optimization of language models, allowing them to run efficiently on a wide range of devices while maintaining high performance. Through techniques like weight sharing, matrix decomposition, and sparse representations, compressed models become more practical for real-world applications, enabling the widespread deployment of advanced AI technologies.

Efficient Architectures: Designing and Adopting Resource-Optimized Models

Efficient architectures are fundamental to optimizing language models for inference speed and performance, particularly in resource-constrained environments. By designing or switching to models specifically crafted to be lighter and faster, we can achieve high levels of performance while significantly reducing computational requirements. Notable examples include streamlined versions of the Transformer architecture such as MobileBERT and TinyBERT. Here’s a detailed look at how efficient architectures work and their benefits.

Key Strategies for Efficient Architectures

1. Reducing the Number of Parameters:

• Smaller Model Sizes: Efficient architectures often involve reducing the total number of parameters. This can be achieved by designing smaller models from scratch or by modifying existing models to have fewer layers or smaller hidden dimensions.
• Example: MobileBERT retains the core architecture of BERT but with significantly fewer parameters, enabling it to run efficiently on mobile devices.

2. Optimizing Layer Structures:

• Simplified Layers: Efficient models often use simpler layer structures that require fewer computations. For example, replacing standard Transformer layers with more compact alternatives.
• Example: TinyBERT compresses the BERT model using techniques like matrix decomposition and parameter sharing to maintain performance while reducing complexity.

3. Parameter Sharing:

• Shared Weights: Some models share parameters across different layers or time steps, reducing the total number of unique parameters.
• Example: In certain versions of efficient Transformers, parameters are shared across layers to reduce the overall parameter count without significantly impacting performance.

4. Distilling Knowledge:

• Teacher-Student Frameworks: Using knowledge distillation, a smaller student model is trained to mimic the performance of a larger teacher model, inheriting its capabilities but with a more efficient structure.
• Example: TinyBERT uses knowledge distillation to transfer knowledge from a larger BERT model, achieving similar performance with a much smaller architecture.

5. Combining Techniques:

• Hybrid Approaches: Efficient architectures often combine multiple optimization techniques, such as pruning, quantization, and parameter sharing, to achieve the best trade-off between performance and efficiency.
• Example: MobileBERT combines knowledge distillation, parameter sharing, and other techniques to create a highly efficient model suitable for mobile devices.

Benefits of Efficient Architectures

1. Reduced Computational Load:

• Efficient architectures lower the computational requirements, making it feasible to deploy complex models on devices with limited processing power, such as smartphones and IoT devices.

2. Faster Inference Times:

• By reducing the number of parameters and optimizing layer structures, these models can achieve faster inference times, which is critical for real-time applications.

3. Lower Memory Footprint:

• Efficient models require less memory, enabling their deployment in environments where memory is a limiting factor, such as embedded systems and edge devices.

4. Energy Efficiency:

• With reduced computational complexity and memory requirements, efficient architectures consume less power, which is essential for battery-operated devices and large-scale deployment in data centers aiming to reduce energy costs.

Notable Efficient Architectures

1. MobileBERT:

• Design: MobileBERT is a compact version of BERT designed specifically for mobile devices. It employs a bottleneck structure to reduce parameter count and computational cost while maintaining high accuracy.
• Performance: MobileBERT offers performance close to that of BERT with significantly reduced latency and memory usage.

2. TinyBERT:

• Design: TinyBERT is a smaller, faster version of BERT created using knowledge distillation and other model compression techniques. It maintains the essential features of BERT while being more resource-efficient.
• Performance: TinyBERT achieves a similar level of accuracy to BERT but with a much smaller model size and faster inference times.

3. DistilBERT:

• Design: DistilBERT is another compact version of BERT that uses knowledge distillation to reduce the number of layers by half while preserving about 97% of BERT’s performance.
• Performance: DistilBERT runs approximately 60% faster and uses 40% less memory than BERT, making it suitable for resource-constrained applications.

Challenges and Considerations

1. Balancing Performance and Efficiency:

• Designing efficient architectures requires careful balancing of model complexity and performance. Aggressive reduction in parameters and layers can lead to a significant drop in accuracy.

2. Specialized Training Techniques:

• Efficient architectures often require advanced training techniques such as knowledge distillation and parameter sharing, which may complicate the training process and require more expertise.

3. Hardware Compatibility:

• The benefits of efficient architectures are maximized when supported by hardware optimized for such models. Ensuring compatibility with existing hardware infrastructure is crucial for deployment.

4. Scalability:

• Efficient models need to be scalable across different devices and platforms. Ensuring that they can be effectively deployed in diverse environments is essential for practical applications.

Efficient architectures play a critical role in optimizing language models for deployment in real-world scenarios. By designing models that are smaller, faster, and more resource-efficient, we can extend the reach of advanced AI technologies to a broader range of applications and devices, ensuring that high-performance language processing is accessible and practical in a variety of contexts.

Batching Inference: Maximizing Hardware Utilization and Throughput

Batching inference is a technique used to enhance the efficiency and performance of language models during inference by processing multiple inputs simultaneously in a single batch. This method is particularly effective on hardware accelerators like GPUs and TPUs, which are designed to handle parallel computations efficiently. Here’s an in-depth exploration of how batching inference works and its benefits.

How Batching Inference Works

1. Simultaneous Processing:

• Instead of processing each input sequentially, batching inference involves grouping multiple inputs together and processing them in parallel. This takes advantage of the parallel processing capabilities of modern hardware.
• For example, instead of running 10 separate inference tasks one after another, batching inference processes all 10 inputs at the same time.

2. Batch Size Selection:

• The number of inputs processed in one batch is referred to as the batch size. Selecting an optimal batch size is crucial for maximizing throughput without exhausting hardware resources.
• Considerations: Larger batch sizes typically improve hardware utilization but require more memory. The optimal batch size depends on the specific hardware and the complexity of the model.

3. Implementation in Frameworks:

• Most deep learning frameworks, such as TensorFlow and PyTorch, provide built-in support for batching. These frameworks allow users to specify batch sizes and automatically handle the parallel processing of inputs.
• Example: In PyTorch, the DataLoader class can be used to load data in batches, and models can be configured to process these batches efficiently.

Benefits of Batching Inference

1. Increased Throughput:

• By processing multiple inputs simultaneously, batching significantly increases the number of inferences the model can perform in a given time period, leading to higher throughput.
• This is especially beneficial for applications that require processing large volumes of data quickly, such as real-time analytics or high-traffic web services.

2. Maximized Hardware Utilization:

• Hardware accelerators like GPUs and TPUs are optimized for parallel computation. Batching allows these devices to operate at their full capacity, making the most of their computational power.
• Efficient utilization of hardware resources reduces idle time and ensures that the computational capabilities of the hardware are fully leveraged.

3. Reduced Latency per Batch:

• Although individual inputs may experience slightly higher latency due to batching, the overall latency per batch is reduced. This trade-off is often acceptable in scenarios where throughput is prioritized over individual response times.

4. Lower Computational Cost:

• Batching can reduce the overall computational cost by minimizing the overhead associated with processing each input separately. This includes reducing the overhead of loading data, initializing computations, and handling results.
• The economies of scale achieved through batching can lead to cost savings, particularly in cloud-based environments where computational resources are billed based on usage.

Challenges and Considerations

1. Memory Limitations:

• Larger batch sizes require more memory, which can be a constraint, especially for high-capacity models or on devices with limited memory.
• Solution: Careful tuning of the batch size to balance memory usage and throughput is necessary. In some cases, gradient checkpointing or other memory optimization techniques can be employed.

2. Latency Sensitivity:

• For real-time applications where individual latency is critical (e.g., interactive systems), batching might introduce unacceptable delays.
• Solution: Adaptive batching techniques can be used, where the batch size is dynamically adjusted based on the current load and latency requirements.

3. Variable Input Sizes:

• Handling variable-sized inputs within a batch can be challenging. Models need to be able to process batches efficiently even when inputs have different shapes or lengths.
• Solution: Padding or bucketing strategies can be used to ensure that inputs within a batch have compatible dimensions.

4. Framework and Infrastructure Compatibility:

• Ensuring that the existing infrastructure and frameworks support efficient batching is crucial. This includes optimizing data pipelines and ensuring that the computational graph is designed to handle batches effectively.

Applications and Impact

1. High-Throughput Applications:

• Batching inference is particularly beneficial for applications that need to process large volumes of data in real-time, such as online recommendation systems, search engines, and large-scale language processing tasks.

Cloud Services:

• Cloud-based AI services can leverage batching to reduce operational costs and improve service efficiency. By processing requests in batches, cloud providers can offer more cost-effective solutions to their customers.

2. Batch Processing Systems:

• Systems designed for batch processing, such as data analytics platforms, can significantly benefit from batching inference. These systems can handle large datasets more efficiently by processing them in parallel.

Batching inference is a crucial technique for optimizing the performance and efficiency of language models, particularly when deployed on powerful hardware accelerators like GPUs and TPUs. By processing multiple inputs simultaneously, batching maximizes hardware utilization, increases throughput, and reduces computational costs, making it an essential strategy for high-performance AI applications.

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Published via Towards AI