## 5 Conclusions  
The ASIC design of computational units is vital for processors, especially in contemporary technologies. The research work's prime focus was designing, developing, and implementing an ASIC-based computational unit using the concepts of low-power VLSI, mainly approximate computing, multipliers based on UT s  
Figure 19: Physical Layout Design of the computational unit used for image compression. Figure 18: Original and reconstructed CT scan images obtained using image compression. operand isolation, and low-power multipliers for stand-alone medical image processing applications. This research covers implementing an ASIC-based computational unit for medical image compression applications. The synthesis was performed with standard semi-custom ASIC design flow using the Genus tool of Cadence. In addition, 45 nm spdk technology libraries were used to synthesize the image compression architecture designed using the computational unit with [PERSON]'s algorithm, resulting in improved performance compared to existing architectures. The PSNR and SSIM of the proposed design were better than the existing architectures due to the incorporation of the computational unit. A significant reduction in PDP and an improved PSNR for VLSI implementations was observed in the synthesis results when carried out on a medical image  
The research work can be explored further by including the ET adders in convolution or digital filter applications involving addition. Future work involves replacing the standard libraries with the optimized digital libraries from Taiwan Semiconductor Manufacturing Company Limited (TSMC) and realizing the design up to chip fabrication. The design can be extended further to make it suitable for the high real-time performance required for video compression coding. The incorporation of low-power techniques can be explored to reduce power. Other multiplier and adder designs can be analyzed to form different combinations of computational units suitable for image processing applications. Different signal processing algorithms can also be explored and implemented using the proposed computational unit. The developed computational unit for image compression that was utilized in the DCT algorithm demonstrates significant potential for direct application in medical image compression. The DCT-based computational unit can substantially reduce the storage requirements, enabling healthcare providers to maintain comprehensive image archives without the necessity of extensive physical storage infrastructure. The hospitals can save on storage costs and enhance data management practices, facilitating quicker retrieval and review of patient images by efficiently compressing images. Compressing medical images reduces their file size, which can be advantageous for encryption processes. Smaller file sizes can be encrypted more quickly and with potentially less computational overhead, thus enhancing the overall security of medical image data. Ensuring the confidentiality and integrity of medical images is critical, and the DCT-based computational unit can play a vital role in facilitating secure storage and transmission protocols. The computational unit can be used to efficiently handle medical images on mobile platforms, allowing healthcare providers to review and share images using smartphones and tablets. This flexibility supports a wide range of healthcare scenarios, from emergency response to routine check-ups, enhancing the accessibility and responsiveness of medical services. In wearable medical devices, DCT compression can compress and transmit data, reducing power consumption and increasing device lifespan. The implementation of this computational unit not only addresses current challenges but also paves the way for future advancements in medical image processing. Continued development and optimization could lead to even more efficient compression algorithms, further enhancing the utility and applicability of this technology in diverse medical environments.