View source for 20240012871. ITERATION ENGINE FOR THE COMPUTATION OF LARGE KERNELS IN CONVOLUTIONAL ACCELERATORS simplified abstract (STMICROELECTRONICS S.r.l.)

=ITERATION ENGINE FOR THE COMPUTATION OF LARGE KERNELS IN CONVOLUTIONAL ACCELERATORS=


==Organization Name==

[[:Category:STMICROELECTRONICS S.r.l.|STMICROELECTRONICS S.r.l.]]


[[Category:STMICROELECTRONICS S.r.l.]]

==Inventor(s)==

[[:Category:Antonio De Vita of Milano (IT)|Antonio De Vita of Milano (IT)]][[Category:Antonio De Vita of Milano (IT)]]

[[:Category:Thomas Boesch of Rovio (CH)|Thomas Boesch of Rovio (CH)]][[Category:Thomas Boesch of Rovio (CH)]]

[[:Category:Giuseppe Desoli of San Fermo Della Battaglia (IT)|Giuseppe Desoli of San Fermo Della Battaglia (IT)]][[Category:Giuseppe Desoli of San Fermo Della Battaglia (IT)]]

==ITERATION ENGINE FOR THE COMPUTATION OF LARGE KERNELS IN CONVOLUTIONAL ACCELERATORS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240012871 titled 'ITERATION ENGINE FOR THE COMPUTATION OF LARGE KERNELS IN CONVOLUTIONAL ACCELERATORS

==Simplified Explanation==

The abstract describes a convolutional accelerator that performs convolution operations on a streaming feature data tensor using a kernel. It includes a feature line buffer, a kernel buffer, a multiply-accumulate cluster, and iteration control circuitry.

* The convolutional accelerator convolves a kernel with a streaming feature data tensor.
* The kernel is decomposed into multiple sub-kernels.
* The sub-kernels are iteratively convolved with respective sub-tensors of the streamed feature data tensor.
* The iteration control circuitry defines windows of the streamed feature data tensors corresponding to the sub-tensors.

Potential Applications:
* Image and video processing: The convolutional accelerator can be used for tasks like object detection, image recognition, and video analysis.
* Deep learning: It can accelerate the computation of convolutional neural networks (CNNs) used in various applications such as natural language processing and computer vision.

Problems Solved:
* Efficient convolution: The accelerator optimizes the convolution process by decomposing the kernel and convolving sub-kernels with sub-tensors, reducing computational complexity.
* Real-time processing: The streaming feature data tensor allows for real-time processing of data, enabling applications that require low latency.

Benefits:
* Faster processing: The accelerator improves the speed of convolution operations, enabling quicker analysis and decision-making.
* Resource efficiency: By decomposing the kernel and using sub-tensors, the accelerator reduces memory and computational requirements.
* Real-time capabilities: The streaming feature data tensor and iteration control circuitry enable real-time processing, making it suitable for time-sensitive applications.


==Original Abstract Submitted==

a convolutional accelerator includes a feature line buffer, a kernel buffer, a multiply-accumulate cluster, and iteration control circuitry. the convolutional accelerator, in operation, convolves a kernel with a streaming feature data tensor. the convolving includes decomposing the kernel into a plurality of sub-kernels and iteratively convolving the sub-kernels with respective sub-tensors of the streamed feature data tensor. the iteration control circuitry, in operation, defines respective windows of the streamed feature data tensors, the windows corresponding to the sub-tensors.



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