In the conventional computing architecture (i.e., Von Neumann architecture), processing elements and memory are separated. MAC operations require massive data transfer between processing elements and memory, which consumes a huge amount of power. Recently, processing-in-memory (PIM) architectures have...
ME is a memory and computation consuming process which accounts for more than 50% of the total running time of HEVC. To conquer the memory and computation challenges, this paper presents ReME, a highly paralleled processing-in-memory (PIM) architecture for the ME process based on resistive ...
Resistive Random-Access Memory (ReRAM) based Processing-in-Memory (PIM) frameworks are proposed to accelerate the working process of DNN models by eliminating the data movement between the computing and memory units. To further mitigate the space and energy consumption, DNN model weight sparsity ...
However, graph processing on traditional architectures issues many random and irregular memory accesses, leading to a huge number of data movements and the consumption of very large amounts of energy. To minimize the waste of memory bandwidth, we investigate utilizing processing-in-memory (PIM), ...
memory.ReRAM-based processing-in-memory(PIM)can resolve this problem by processing embedding vectors where they are stored.However,the embedding table can easily exceed the capacity limit of a monolithic ReRAM-based PIM chip,which induces off-chip accesses that may offset the PIM profits.The...
ERA-BS: Boosting the Efficiency of ReRAM-Based PIM Accelerator With Fine-Grained Bit-Level Sparsitydoi:10.1109/TC.2023.3290869Processing-in-memoryneural networkhardware acceleratorbit-level sparsityResistive Random-Access-Memory (ReRAM) crossbar is one of the most promising neural network accelerators, ...
Emerging resistive random-access memory (ReRAM) based processing-in-memory (PIM) accelerators have been increasingly explored in recent years because they can efficiently perform in-situ matrix-vector multiplication (MVM) operations involved in a wide spectrum of artificial neural networks. However, ...
ME is a memory and computationally intensive process which consumes more than 50% of the total running time of HEVC. To remedy the memory and computation challenges, in this paper, we present ReME, a highly paralleled Processing-In-Memory accelerator for ME based on ReRAM. In ReME, the ...
Among recent technologies to accelerate DNN, resistive memory (ReRAM)-based processing-in-memory (PIM) emerged as a promising solution for DNN inference due to its high efficiency for matrix-based computation. We face two major technical challenges in extending the use of ReRAM-based accelerators...
ME is a memory and computation consuming process which accounts for more than 50% of the total running time of HEVC. To conquer the memory and computation challenges, this paper presents ReME, a highly paralleled processing-in-memory (PIM) architecture for the ME process based on resistive ...