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Compute-in-memory (CiM) architecture is an emerging energy-efficient processing paradigm that has attracted widespread attention in AI and Internet of Things (IoT) applications. To protect statically stored sensitive data in CiM, designers have ...

This paper introduces a Computing-In-Memory based Spiking Neural Network (SNN) architecture for cryogenic operation of CMOS (Cryo-SNN). The paper demonstrates design strategies to improve energy efficiency of Cryo-SNN by coupling low-voltage operation at ...

The prediction accuracy of deep neural networks (DNNs) deployed at the edge can deteriorate over time due to shifts in the data distribution. For heightened robustness, it’s crucial for DNNs to continually refine and improve their predictive capabilities. ...

Bulk bitwise operations are commonplace in application domains such as databases, web search, cryptography, and image processing. The ever-growing volume of data and processing demands of these domains often result in high energy consumption and latency ...

In-sensor computing has emerged as a promising approach to mitigating huge data transmission costs between sensors and processing units. Recently, the emerging application scenarios have raised more demands of sensory technology for large-area and ...

Compute-in-memory based binary neural networks or CiM-BNNs offer high energy/area efficiency for the design of edge deep neural network (DNN) accelerators, with only a mild accuracy reduction. However, for successful deployment, the design of CiM-BNNs ...

Using non-volatile "capacitive" crossbar arrays for compute-in-memory (CIM) offers higher energy and area efficiency compared to "resistive" crossbar arrays. However, the impact of device-to-device (D2D) variation and temporal noise on the system-level ...

Deploying a lightweight quantized model in compute-in-memory (CIM) might result in significant accuracy degradation due to reduced signal-noise rate (SNR). To address this issue, this paper presents ZEBRA, a zero-bit robust-accumulation CIM approach, ...

Time-domain (TD) computing has attracted attention for its high computing efficiency and suitability for applications on energy-constrained edge devices. In this paper, we present a time-domain compute-in-memory (TDCIM) macro for binary neural networks (...

Computing-in-memory has been widely explored to accelerate DNN. However, most existing CIM cannot store all NN weights due to limited SRAM capacity for edge AI devices, inducing a large amount off-chip DRAM access. In this paper, a new computing in ReRAM-...

Compute-in-Memory (CiM) hardware has shown great potential in accelerating Deep Neural Networks (DNNs). However, most CiM accelerators for matrix vector multiplication rely on costly analog to digital converters (ADCs) which becomes a bottleneck in ...

Recently, analog compute-in-memory (CIM) architectures based on emerging analog non-volatile memory (NVM) technologies have been explored for deep neural networks (DNNs) to improve scalability, speed, and energy efficiency. Such architectures, however, ...

Processing-In-Memory (PIM) technologies is one of most promising candidates for AIoT applications due to its attractive characteristics, such as low computation latency, large throughput and high power efficiency. However, how to efficiently utilize PIM ...

Compute-in-memory (CIM) is an attractive solution to address the “memory wall” challenges for the extensive computation in deep learning hardware accelerators. For custom ASIC design, a specific chip instance is restricted to a specific network during ...

Compute-in-memory (CIM) is a promising approach that exploits the analog computation inside the memory array to speed up the vector-matrix multiplication (VMM) for deep neural network (DNN) inference. SRAM has been demonstrated as a mature candidate for ...

3D NAND Flash memory has been proposed as an attractive candidate of inference engine for deep neural network (DNN) owing to its ultra-high density and commercially matured fabrication technology. However, the peripheral circuits require to be modified ...

Compute-in-memory (CiM) is a promising method for mitigating the memory wall problem in data-intensive applications. The proposed bitwise logic-in-memory (BLiM) is targeted at data intensive applications, such as database, data encryption. This work ...

Rapid development in deep neural networks (DNNs) is enabling many intelligent applications. However, on-chip training of DNNs is challenging due to the extensive computation and memory bandwidth requirements. To solve the bottleneck of the memory wall ...

As CMOS technology advances, the performance gap between the CPU and main memory has not improved. Furthermore, the hardware deployed for Internet of Things (IoT) applications need to process ever growing volumes of data, which can further exacerbate the ...