Search results
Results from the WOW.Com Content Network
Circular buffering makes a good implementation strategy for a queue that has fixed maximum size. Should a maximum size be adopted for a queue, then a circular buffer is a completely ideal implementation; all queue operations are constant time. However, expanding a circular buffer requires shifting memory, which is comparatively costly.
In computer science, a data buffer (or just buffer) is a region of memory used to store data temporarily while it is being moved from one place to another. Typically, the data is stored in a buffer as it is retrieved from an input device (such as a microphone) or just before it is sent to an output device (such as speakers); however, a buffer may be used when data is moved between processes ...
The frequency of such pipeline stalls can be reduced by providing space for more than one item in the input buffer of that stage. Such a multiple-item buffer is usually implemented as a first-in, first-out queue. The upstream stage may still have to be halted when the queue gets full, but the frequency of those events will decrease as more ...
For FIFO input buffers, a simple model of fixed-sized cells to uniformly distributed destinations, causes the throughput to be limited to 58.6% of the total as the number of links becomes large. [1] One way to overcome this limitation is by using virtual output queues. [2] Only switches with input buffering can suffer HOL blocking.
Fetching the instruction opcodes from program memory well in advance is known as prefetching and it is served by using a prefetch input queue (PIQ). The pre-fetched instructions are stored in a queue. The fetching of opcodes well in advance, prior to their need for execution, increases the overall efficiency of the processor boosting its speed ...
Feature learning is intended to result in faster training or better performance in task-specific settings than if the data was input directly (compare transfer learning). [1] In machine learning (ML), feature learning or representation learning [2] is a set of techniques that allow a system to automatically discover the representations needed ...
Autoassociative self-supervised learning is a specific category of self-supervised learning where a neural network is trained to reproduce or reconstruct its own input data. [8] In other words, the model is tasked with learning a representation of the data that captures its essential features or structure, allowing it to regenerate the original ...
During the deep learning era, attention mechanism was developed to solve similar problems in encoding-decoding. [1] In machine translation, the seq2seq model, as it was proposed in 2014, [24] would encode an input text into a fixed-length vector, which would then be decoded into an output text. If the input text is long, the fixed-length vector ...