電阻式隨機存取記憶體（ReRAM）已經成為一種有潛力的解決方案，通過在記憶體中進行處理的操作來加速深度神經網路（DNN）的推理。然而，在這樣的加速器上實施多個神經網路推理會遇到資源分配和調度問題。在這項研究中，我們首先提出了一種權重複製機制，透過考慮層之間的數據依賴關係和網路之間的資源競爭，為每個網路分配適當的資源，目的為最小化多個網路推理的延遲。為了進一步提升能量效率，我們使用可調整式類比-數位轉換器（ADC），在運行時分割運算單元，以最小化能量消耗。實驗結果表明，提出的方法在各種深度神經網路工作負載中可顯著節省能源並減少延遲，與現有基於 ReRAM 加速器的技術相比，ENGINER 減少了高達99% 的延遲，同時節省了95% 的能源。

Resistive random-access memory (ReRAM) has emerged as a promising solution to
accelerate the inference of deep neural networks (DNNs) through processing-in-memory
(PIM) operations. However, implying multiple neural network inferences on such an accelerator suffers from resource allocation and scheduling issues. In this study, we first propose a weight duplication mechanism to allocate proper resources for each network by considering the data dependency between layers and the resource contention between networks, aiming to minimize the latency of the given multiple network inference. To further achieve energy efficiency, we perform run-time operation splitting using configurable analog-to-digital converters (ADCs) to minimize energy consumption. Experimental results demonstrate that the proposed approach yields substantial energy savings and latency reduction across various deep neural network workloads, ENGINER reduce up to 99% of latency while saving 95% of energy compared to the state-of-the-art ReRAM-based accelerator

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