Resource-constrained IoT devices, such as sensors and actuators, have become ubiquitous
in recent years. This has led to the generation of large quantities of data in real-time, which …

Recent years have seen a phenomenal rise in the performance and applications of
transformer neural networks. The family of transformer networks, including Bidirectional …

The attention mechanism is becoming increasingly popular in Natural Language Processing
(NLP) applications, showing superior performance than convolutional and recurrent …

Current low-precision quantization algorithms often have the hidden cost of conversion back
and forth from floating point to quantized integer values. This hidden cost limits the latency …

Transformer-based large language models (LLMs) have achieved great success with the
growing model size. LLMs' size grows by 240× every two years, which outpaces the …

Transformer model is becoming prevalent in various AI applications with its outstanding
performance. However, the high cost of computation and memory footprint make its …

Quantization is a technique to reduce the computation and memory cost of DNN models,
which are getting increasingly large. Existing quantization solutions use fixed-point integer …

As CNNs are being extensively employed in high performance and safety-critical
applications that demand high reliability, it is important to ensure that they are resilient to …

This paper presents a new methodology to alleviate the fundamental trade-off between
accuracy and latency in spiking neural networks (SNNs). The approach involves decoding …

In recent years, transformer models have revolutionized natural language processing (NLP)
and shown promising performance on computer vision (CV) tasks. Despite their …