US 12,277,491 B2
Encoded host to DLA traffic
Poorna Kale, Folsom, CA (US); and Saideep Tiku, Fort Collins, CO (US)
Assigned to Micron Technology, Inc., Boise, ID (US)
Filed by Micron Technology, Inc., Boise, ID (US)
Filed on May 10, 2021, as Appl. No. 17/316,496.
Prior Publication US 2022/0358351 A1, Nov. 10, 2022
Int. Cl. G06N 3/063 (2023.01); G06F 12/123 (2016.01); G06F 21/14 (2013.01); G06F 21/60 (2013.01); G06F 21/62 (2013.01); G06N 3/04 (2023.01); G06N 3/045 (2023.01); G06N 3/0455 (2023.01); G06N 3/08 (2023.01); G11C 16/08 (2006.01)
CPC G06N 3/063 (2013.01) [G06F 12/125 (2013.01); G06F 21/14 (2013.01); G06F 21/6218 (2013.01); G06N 3/04 (2013.01); G06N 3/045 (2023.01); G06N 3/0455 (2023.01); G06N 3/08 (2013.01); G11C 16/08 (2013.01); G06F 21/606 (2013.01)] 23 Claims
OG exemplary drawing
 
1. An apparatus, comprising:
an array of memory cells;
a deep learning accelerator (DLA) coupled to the array; and
a controller coupled to the array and to the DLA, wherein the controller is configured to:
receive encoded data from a host;
store the encoded data in the array; and
wherein the DLA is configured to:
responsive to receiving signals from the controller, access the encoded data from the array;
decode the encoded data utilizing an autoencoder wherein the encoded data is decoded by a decoder of the autoencoder implemented by the DLA to generate decoded data and wherein the decoder of the autoencoder is implemented as an artificial neural network (ANN); and
perform a plurality of operations on the decoded data.