A 28nm 16.9-300TOPS/W computing-in-memory processor supporting floating-point NN inference/training with intensive-CIM sparse-digital architecture. In Proc. 2023 IEEE International Solid-State Circuits Conferenc
内存墙的存在使得处理器无法充分发挥其计算能力,因为它经常处于等待数据的状态。为了解决这一问题,所以为了克服冯诺依曼瓶颈并满足对更优计算性能日益增长的需求,存内计算(Computing-In-Memory,CIM)技术成为了一种可能的解决方案。存内计算技术不仅无需频繁在存储单元和计算单元之间进行数据传输,还能够实现多横向或多纵向...
In-memory-computing (IMC) architecture has been proposed as an alternative to cope with the memory wall of von-Neumann computing architecture. The IMC architecture embeds logic into the memory array to reduce the data transfer between the processor and memory. An IMC memory can be operated in ...
COMPASS: SRAM-Based Computing-in-Memory SNN Accelerator with Adaptive Spike Speculation. Proceedings of the 57th IEEE/ACM International Symposium on Microarchitecture (MICRO'24). November 2024.(标 * 为通信作者) 发布于 2024-08-24 16:48・北京 人工智能...
弱化或消除“存储墙”及“功耗墙”问题的方法是采用存内计算Computing-in-Memory(CIM)结构。其核心思想是将部分或全部的计算移到存储中,让存储单元具有计算能力,数据不需要单独的运算部件来完成计算,而是在存储单元中完成存储和计算,消除了数据访存延迟和功耗,是一种真正意义上的存储与计算融合。同时,由于计算完全依赖...
7.7 CV-CIM: A 28nm XOR-Derived Similarity-Aware Computation-in-Memory for Cost-Volume Construction 7.8 A 22nm Delta-Sigma Computing-In-Memory (ΔΣCIM) SRAM Macro with Near-Zero-Mean Outputs and LSB-First ADCs Achieving 21.38TOPS/W for 8b-MAC Edge AI Processing 7.9 CTLE-Ising: A 1440-Spi...
基于MRAM(磁性随机存取存储器)的存算一体(MRAM-in-Memory Computing)是一种新型的计算模式,它结合了内存和计算的功能,利用MRAM的非易失性和快速读写特性,在存储器内部进行计算操作,从而提高计算效率和能源利用率。 MRAM作为一种新型的存储技术,具有快速的读/写速度、低功耗和非易失性等优点。这些特性使得MRAM非常适...
An Energy-Efficient Computing-in-Memory (CiM) cell design utilizing a Negative Capacitance (NC) FET has been proposed to support computing architectures for Deep Neural Networks (DNNs). The NCFET device characteristics for CiM architectures have been studied to determine an optimal device performance...
论文信息:Zongwu Wang, Fangxin Liu*, Ning Yang, Shiyuan Huang, Haomin Li and Li Jiang*. COMPASS: SRAM-Based Computing-in-Memory SNN Accelerator with Adaptive Spike Speculation. Proceedings of the 57th IEEE/ACM International Symposium on Microarchitecture (MICRO'24). November 2024.(标 * 为通信作...
论文信息:Zongwu Wang, Fangxin Liu*, Ning Yang, Shiyuan Huang, Haomin Li and Li Jiang*. COMPASS: SRAM-Based Computing-in-Memory SNN Accelerator with Adaptive Spike Speculation. Proceedings of the 57th IEEE/ACM International Symposium on Microarchitecture (MICRO'24). November 2024.(标 * 为通信作...