在不修改现有内存硬件的前提下，MVDRAM 仅利用手机、电脑等设备现有的存储硬件，即可实现存内计算。其推理性能可以媲美甚至超过现有专用加速器，显著降低了大模型的部署门槛，有望推动大模型在各类设备上的普及，并为大模型的端侧部署提供创新的解决方案。批量按位累加：实现矩阵乘累加的存内计算 在大模型的运行进程中，矩阵乘无疑是最
硬件架构不能指望计算量一大，就扩展CPU，存储量一大，就用内存堆砌的方式；这是一种对过去架构的严重依...
Despite millions of years of evolution, the fundamental wiring principle of biological brains has been preserved: dense local and sparse global connectivity through synapses between neurons. This persistence indicates the efficiency of this solution in optimizing both computation and the utilization of the...
In-memory computing (IMC) is an emerging non-von Neumann computational paradigm that keeps alive the promise of achieving energy efficiencies on the order of one femtoJoule per operation in a computing system. The key idea is to perform certain computational tasks in place in memory, thereby obv...
A method for accelerating a convolution of a kernel matrix over an input matrix for computation of an output matrix using in-memory computation involves storing in different sets of cells, in an array of cells, respective combinations of elements of the kernel matrix or of multiple kernel ...
In-/Near-Memory Computing 《存内/迳存计算》 作者:Daichi Fujiki, Xiaowei Wang, Arun Subramaniyan, and Reetuparna Das University of Michigan, Ann Arbor 翻译: Yiyang
Modern computers are based on the von Neumann architecture in which computation and storage are physically separated: data are fetched from the memory unit, shuttled to the processing unit (where computation takes place) and then shuttled back to the memory unit to be stored. The rate at which...
The low-precision computational memory unit (right) performs analog in-memory computation using one or multiple memristive arrays. The system bus (middle) implements the overall management (control, data, addressing) between the two units. The purple dotted arrows indicate control communication and ...
In-Memory Computation (IMC) is an emerge architecture for recent AI deep learning filed. Different from traditional computing, IMC could process data in parallel and shorter processing time. In AI neural network system, the weighting is calculated by resistance changing of memory. The key factor ...
The feature of data grids that distinguishes them from distributed caches was their ability to support co-location of computations with data in a distributed context and consequently provided the ability to move computation to data. This capability was the key innovation that addressed the demands of...