Increasing AI Energy Efficiency With Compute In Memory
- 發佈日期 : 2023-11-20
- 資料來源：Semiconductor Engineering
- 瀏覽人次：52
How to process zettascale workloads and stay within a fixed power budget.
Skyrocketing AI compute workloads and fixed power budgets are forcing chip and system architects to take a much harder look at compute in memory (CIM), which until recently was considered little more than a science project.
詳見內文，請點選連結：【Increasing AI Energy Efficiency With Compute In Memory】