Category: Model Optimisation Techniques

AI Accelerator Design

AI accelerator design involves creating specialised hardware that speeds up artificial intelligence tasks like machine learning and deep learning. These devices are built to process large amounts of data and complex calculations more efficiently than general-purpose computers. By focusing on the specific needs of AI algorithms, these accelerators help run AI applications faster and use…

Edge Inference Optimization

Edge inference optimisation refers to making artificial intelligence models run more efficiently on devices like smartphones, cameras, or sensors, rather than relying on distant servers. This process involves reducing the size of models, speeding up their response times, and lowering power consumption so they can work well on hardware with limited resources. The goal is…

Data Encryption Optimization

Data encryption optimisation involves improving the speed, efficiency, and effectiveness of encrypting and decrypting information. It aims to protect data without causing unnecessary delays or using excessive computing resources. Techniques include choosing the right algorithms, reducing redundant steps, and balancing security needs with performance requirements.

Smart Contract Optimization

Smart contract optimization is the process of improving the performance and efficiency of smart contracts, which are self-executing programs on blockchain platforms. This involves making the code use less computing power, storage, and transaction fees, while still achieving the same results. Well-optimised smart contracts are faster, more secure, and cost less to run for users…

Layer 2 Transaction Optimization

Layer 2 transaction optimisation refers to methods and technologies that improve the speed and reduce the cost of transactions on blockchain networks by processing them off the main blockchain, or Layer 1. These solutions use separate protocols or networks to handle transactions, then periodically record summaries or proofs back to the main chain. This approach…

Token Incentive Optimization

Token incentive optimisation is the process of designing and adjusting rewards in digital token systems to encourage desirable behaviours among users. It involves analysing how people respond to different incentives and making changes to maximise engagement, participation, or other goals. This approach helps ensure that the token system remains effective, sustainable, and aligned with the…

Federated Learning Optimization

Federated learning optimisation is the process of improving how machine learning models are trained across multiple devices or servers without sharing raw data between them. Each participant trains a model on their own data and only shares the learned updates, which are then combined to create a better global model. Optimisation in this context involves…

Differential Privacy Optimization

Differential privacy optimisation is a process of adjusting data analysis methods so they protect individuals’ privacy while still providing useful results. It involves adding carefully controlled random noise to data or outputs to prevent someone from identifying specific people from the data. The goal is to balance privacy and accuracy, so the information remains helpful…

Inference Optimization Techniques

Inference optimisation techniques are methods used to make machine learning models run faster and use less computer power when making predictions. These techniques focus on improving the speed and efficiency of models after they have already been trained. Common strategies include reducing the size of the model, simplifying its calculations, or using special hardware to…