公开(公告)号：US20230334329A1

公开(公告)日：2023-10-19

申请号：US18157983

申请日：2023-01-23

Applicant: Korea Advanced Institute of Science and Technology

Inventor： Joo-Young KIM ,  Je YANG

IPC: G06N3/0495 ,  G06N3/092

CPC classification number: G06N3/092 ,  G06N3/0495

Abstract: The present disclosure provides a system for accelerating multi-agent reinforcement learning through sparsity processing and an operating method thereof and proposes an acceleration system, which can analyze a weight pruning algorithm capable of guaranteeing accuracy suitably for characteristics of multi-agent reinforcement learning and includes an on-chip encoding unit, a sparse weight workload allocation unit, and sparsity parallel processing architecture through vector processing, which can effectively support the weight pruning algorithm, and an operating method of the system. Furthermore, the present disclosure proposes an acceleration platform that constitutes a circuit in a way to be suitable for a deep learning model from its initial step while having high throughput and power efficiency by using an FPGA, not a GPU in which several thousands of cores have been integrated and which generate many and consume great power.

公开(公告)号：US20220405642A1

公开(公告)日：2022-12-22

申请号：US17825911

申请日：2022-05-26

Applicant: SK hynix Inc. ,  Korea Advanced Institute of Science and Technology

Inventor： Je YANG ,  Seongmin HONG ,  Joo-Young KIM

IPC: G06N20/00

Abstract: A reinforcement learning device includes a computation circuit configured to perform an operation between a weight matrix and an input activation vector and to apply an activation function on an output of the operation to generate an output activation vector. The computation circuit quantizes the input activation vector when a quantization delay time has elapsed since beginning of a learning operation and does not quantize the input activation vector otherwise.