  Deep learning models have achieved tremendous success in most of the
industries in recent years. The evolution of these models has also led to an
increase in the model size and energy requirement, making it difficult to
deploy in production on low compute devices. An increase in the number of
connected devices around the world warrants compressed models that can be
easily deployed at the local devices with low compute capacity and power
accessibility. A wide range of solutions have been proposed by different
researchers to reduce the size and complexity of such models, prominent among
them are, Weight Quantization, Parameter Pruning, Network Pruning, low-rank
representation, weights sharing, neural architecture search, knowledge
distillation etc. In this research work, we investigate the performance impacts
on various trained deep learning models, compressed using quantization and
pruning techniques. We implemented both, quantization and pruning, compression
techniques on popular deep learning models used in the image classification,
object detection, language models and generative models-based problem
statements. We also explored performance of various large language models
(LLMs) after quantization and low rank adaptation. We used the standard
evaluation metrics (model's size, accuracy, and inference time) for all the
related problem statements and concluded this paper by discussing the
challenges and future work.
