Spiking neural networks (SNNs), which mimic biological neural systems to convey information via discrete spikes, are well-known as brain-inspired models with excellent computing efficiency. By utilizing the surrogate gradient estimation for ...

Spiking neural networks (SNNs) are becoming a promising alternative to conventional artificial neural networks (ANNs) due to their rich neural dynamics and the implementation of energy-efficient neuromorphic chips. However, the non-differential ...$$$$$$$$$$$$$$

Attention-based models, such as Transformer and BERT, have achieved remarkable success across various tasks. However, their deployment is hindered by challenges such as high memory requirements, long inference latency, and significant power ...${}^{}$${}^{}$$\mathrm{}$$\mathrm{}$$\mathrm{}$$\mathrm{}$$\mathrm{}$$\mathrm{}$

Processing-in-Memory (PIM) has been widely explored for accelerating data-intensive machine learning computation that mainly consists of general-matrix-multiplication (GEMM), by mitigating the burden of data movements and exploiting the ultra-high memory ...

Recent advances in processing-in-cache (PIC) have enabled generalpurpose, high-performance computation with bit-serial computing techniques. Its outstanding performance relies on efficient hardware design, and also the software stack (i.e., Logic ...

Model compression has been extensively investigated for supporting efficient neural network inference on edge-computing platforms due to the huge model size and computation amount. Recent researches embrace joint-way compression across multiple ...

Event-driven spiking neural networks (SNNs) have shown great promise for being strikingly energy-efficient. SNN neurons integrate the spikes, accumulate the membrane potential, and fire output spike when the potential exceeds a threshold. Existing SNN ...

Deep hashing has gained growing momentum in large-scale image retrieval. However, deep hashing is computation- and memory-intensive, which demands hardware acceleration. The unique process of hash sequence computation in deep hashing is non-trivial to ...

The Resistive Random-Access-Memory (ReRAM) in crossbar structure has shown great potential in accelerating the vector-matrix multiplication, owing to the fascinating computing complexity reduction (from O(n²) to O(1)). Nevertheless, the ReRAM cells ...

Models based on the attention mechanism, i.e. transformers, have shown extraordinary performance in Natural Language Processing (NLP) tasks. However, their memory footprint, inference latency, and power consumption are still prohibitive for efficient ...

Accelerating the neural network inference by FPGA has emerged as a popular option, since the reconfigurability and high performance computing capability of FPGA intrinsically satisfies the computation demand of the fast-evolving neural algorithms. ...

Binary Neural Networks (BNNs) have attracted tremendous attention in ReRAM-based Process-In-Memory (PIM) systems, since they significantly simplify the hardware-expensive peripheral circuits and memory footprint. Meanwhile, BNNs are proven to have ...

Graph Convolutional Network (GCN) is a promising but computing- and memory-intensive learning model. Processing-in-memory (PIM) architecture based on the ReRAM crossbar is a natural fit for GCN inference. It can reduce the data movements and compute the ...

Resistive Random-Access-Memory (ReRAM) crossbar is one of the most promising neural network accelerators, thanks to its in-memory and in-situ analog computing abilities for Matrix Multiplication-and-Accumulations (MACs). Nevertheless, the number of rows ...

Graph Convolutional Networks (GCNs) have become the prevailing approach to efficiently learn representations from graph-structured data. Current GCN models adopt a neighborhood aggregation mechanism based on two primary operations, aggregation and ...

Field-programmable Gate Array (FPGA) is a high-performance computing platform for Convolution Neural Networks (CNNs) inference. Winograd algorithm, weight pruning, and quantization are widely adopted to reduce the storage and arithmetic overhead of CNNs ...

As mobile devices continuously generate streams of images and videos, a new class of mobile deep vision applications are rapidly emerging, which usually involve running deep neural networks on these multimedia data in real-time. To support such ...

This work presents HRAM, a SRAM-based hybrid memory bit-cell for energy-efficient in-memory computing purpose. The HRAM bit-cell consists of conventional 6T-SRAM for static data storage, and extra one accessing transistor and capacitor for caching data ...

ReRAM-based deep neural network (DNN) accelerator shows enormous potential because of ReRAM's high computational-density and power-efficiency. A typical feature of DNNs is that weight matrix size varies across diverse DNNs and DNN layers. However, ...