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General-purpose computing systems employ memory hierarchies to provide the appearance of a single large, fast, coherent memory. In special-purpose CPUs, programmers manually manage distinct, non-coherent scratchpad memories. In this article, we combine these mechanisms by adding a virtually addressed, set-associative scratchpad to a general purpose CPU. Our scratchpad exists alongside a traditional cache and is able to avoid many of the programming challenges associated with traditional scratchpads without sacrificing generality (e.g., virtualization). Furthermore, our design delivers increased security and improves performance, especially for workloads with high locality or that interact with nonvolatile memory.

Utilization of online websites to shop for a range of products has been frequent in our day to day lives. As a result, consumer demand is becoming more diverse, making it difficult for a general store to deliver the proper products based on the tastes of its customers. To deliver a favorable buying experience for the consumer, these E-commerce websites use various recommendation system algorithms. Recommendation systems are a tool for dealing with this problem; they allow you to meet consumer’s demands and expectations while also attracting new ones. A product recommendation system is essentially a filtering system that suggests particular things to customers depending on their interests. Recommendation systems have exploded in popularity in recent years with applications in music, news, movies search queries and others. The bulk of today’s E- commerce sites such as Amazon, Flipkart ,Myntra, make use of their own recommendation algorithms to better offer their customers with products they are likely to like .Recommendation engines are data filtering technologies that use algorithms and data to suggest the most relevant items to the user.

One of the most popular domestic animals is the dog. Numerous problems, including population control, a decline in disease outbreaks like rabies, vaccination oversight, and legal ownership, are brought on by the abundance of dogs. There are currently around 180 different dog breeds. Each breed of dog has unique traits and health issues. It is crucial to identify people and their breeds in order to administer the proper therapies and training. Machine learning provides the ability to train algorithms that can tackle the challenges of information classification and prediction based only on newly emerging information as raw data. For the categorization and detection of images, Convolutional Neural Networks (CNNs) provide a single, widely utilized method. In this effort, we establish a CNN-based method for identifying dogs in potentially complicated photos, and as a result, we consider only one of the types of dog breed identification. The graphical depiction demonstrates that the algorithm (CNN) delivers good analysis accuracy for all the examined datasets because the experimental outcome analysis confirmed the conventional metrics.

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