通过基于矩阵外积计算数学原理的浮点/定点存算块架构，矩阵-矩阵-向量计算可通过累加器元件完成。该突破以“A 28nm 72.12TFLOPS/W Hybrid-Domain Outer-Product Based Floating-Point SRAM Computing-in-Memory Macro with Logarithm Bit-Width Residual ADC”为题
通过基于矩阵外积计算数学原理的浮点/定点存算块架构,矩阵-矩阵-向量计算可通过累加器元件完成。 该突破以“A 28nm 72.12TFLOPS/W Hybrid-Domain Outer-Product Based Floating-Point SRAM Computing-in-Memory Macro with Logarithm Bit-Width ResidualADC”为题发表在ISSCC 2024国际会议上,微电子所博士生袁易扬为第一...
通过基于矩阵外积计算数学原理的浮点/定点存算块架构,矩阵-矩阵-向量计算可通过累加器元件完成。 该突破以“A 28nm 72.12TFLOPS/W Hybrid-Domain Outer-Product Based Floating-Point SRAM Computing-in-Memory Macro with Logarithm Bit-Width Residual ADC”为题发表在ISSCC 2024国际会议上,微电子所博士生袁易扬为第...
通过基于矩阵外积计算数学原理的浮点/定点存算块架构,矩阵-矩阵-向量计算可通过累加器元件完成。 该突破以“A 28nm 72.12TFLOPS/W Hybrid-Domain Outer-Product Based Floating-Point SRAM Computing-in-Memory Macro with Logarithm Bit-Width Residual ADC”为题发表在ISSCC 2024国际会议上,微电子所博士生袁易扬为第...
该突破以“A 28nm 72.12TFLOPS/W Hybrid-Domain Outer-Product Based Floating-Point SRAM Computing-in-Memory Macro with Logarithm Bit-Width Residual ADC”为题发表在ISSCC 2024国际会议上,微电子所博士生袁易扬为第一作者,张锋研究员与北京理工大学王兴华教授为通讯作者。该研究得到了科技部重点研发计划、国家自然...
如图所示,当前的主流计算系统所使用的数据处理方案,依赖于数据存储与数据处理分离的体系结构(冯诺依曼架构),为了满足速度和容量的需求,现代计算系统通常采取高速缓存(SRAM)、主存(DRAM)、外部存储(NAND Flash)的三级存储结构。 常见的存储系统架构及存储墙 (全球半导体观察制图)...
看3D NAND的制程,感叹半导体制程的设计巧夺天工。未来那种新兴记忆体能夺取存储级记忆体(storage class memory)的市场区块,甚至最终成为记忆体中计算(computing in memory)的最终人选,就端看有没有机会用真正3D制程来设计结构。
看3D NAND的制程,感叹半导体制程的设计巧夺天工。未来那种新兴记忆体能夺取存储级记忆体(storage class memory)的市场区块,甚至最终成为记忆体中计算(computing in memory)的最终人选,就端看有没有机会用真正3D制程来设计结构。
Summary Recent advancements in 3D NAND flash memory are driven by the need for higher storage density and faster data access speed, particularly to support emerging applications, such as artificial intelligence and memory-centric computing. As 3D NAND flash technology evolves, various technical problems...
The actual density of 3D X-DRAM is dependent on the advancement of 3D NAND process. 3D X-DRAM can provide 8 times more capacity for High Bandwidth Memory (HBM), so a typical HBM capacity for AI chips can be increased from 192 GB to 1.5 TB, resulting in a boosted performance for AI ...