Algorithm/Hardware Co-Design for Few-Shot Learning at the Edge
On-device learning is essential to achieve intelligence at the edge, where it is desirable to
learn from few samples or even just a single sample. Memory-augmented neural networks …
learn from few samples or even just a single sample. Memory-augmented neural networks …
Accuracy Improvement with Weight Mapping Strategy and Output Transformation for STT-MRAM-Based Computing-In-Memory
X Wang, N Wei, S Gao, W Wu… - IEEE Journal on …, 2024 - ieeexplore.ieee.org
This work presents an analog computing-in-memory (CiM) macro with spin-transfer torque
magnetic random access memory (STT-MRAM) and 28-nm CMOS technology. The adopted …
magnetic random access memory (STT-MRAM) and 28-nm CMOS technology. The adopted …
[图书][B] Unlocking the Power of Content Addressable Memory for Memory-Intensive Applications
M Li - 2024 - search.proquest.com
Due to the high cost of data movement in the traditional von Neumann architecture,
particularly in many data-intensive workloads, in-memory computing (IMC), by integrating …
particularly in many data-intensive workloads, in-memory computing (IMC), by integrating …