A survey on optimization techniques for edge artificial intelligence (ai)
C Surianarayanan, JJ Lawrence, PR Chelliah… - Sensors, 2023 - mdpi.com
Artificial Intelligence (Al) models are being produced and used to solve a variety of current
and future business and technical problems. Therefore, AI model engineering processes …
and future business and technical problems. Therefore, AI model engineering processes …
Reconfigurable halide perovskite nanocrystal memristors for neuromorphic computing
Many in-memory computing frameworks demand electronic devices with specific switching
characteristics to achieve the desired level of computational complexity. Existing memristive …
characteristics to achieve the desired level of computational complexity. Existing memristive …
Zero-shot text-to-image generation
Text-to-image generation has traditionally focused on finding better modeling assumptions
for training on a fixed dataset. These assumptions might involve complex architectures …
for training on a fixed dataset. These assumptions might involve complex architectures …
[HTML][HTML] An analog-AI chip for energy-efficient speech recognition and transcription
Abstract Models of artificial intelligence (AI) that have billions of parameters can achieve
high accuracy across a range of tasks,, but they exacerbate the poor energy efficiency of …
high accuracy across a range of tasks,, but they exacerbate the poor energy efficiency of …
Higher-dimensional processing using a photonic tensor core with continuous-time data
New developments in hardware-based 'accelerators' range from electronic tensor cores and
memristor-based arrays to photonic implementations. The goal of these approaches is to …
memristor-based arrays to photonic implementations. The goal of these approaches is to …
Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators
Analog in-memory computing—a promising approach for energy-efficient acceleration of
deep learning workloads—computes matrix-vector multiplications but only approximately …
deep learning workloads—computes matrix-vector multiplications but only approximately …
Fp8 quantization: The power of the exponent
When quantizing neural networks for efficient inference, low-bit integers are the go-to format
for efficiency. However, low-bit floating point numbers have an extra degree of freedom …
for efficiency. However, low-bit floating point numbers have an extra degree of freedom …
Resource-efficient convolutional networks: A survey on model-, arithmetic-, and implementation-level techniques
JK Lee, L Mukhanov, AS Molahosseini… - ACM Computing …, 2023 - dl.acm.org
Convolutional neural networks (CNNs) are used in our daily life, including self-driving cars,
virtual assistants, social network services, healthcare services, and face recognition, among …
virtual assistants, social network services, healthcare services, and face recognition, among …
Training transformers with 4-bit integers
Quantizing the activation, weight, and gradient to 4-bit is promising to accelerate neural
network training. However, existing 4-bit training methods require custom numerical formats …
network training. However, existing 4-bit training methods require custom numerical formats …
A survey on machine learning accelerators and evolutionary hardware platforms
Advanced computing systems have long been enablers for breakthroughs in artificial
intelligence (AI) and machine learning (ML) algorithms, either through sheer computational …
intelligence (AI) and machine learning (ML) algorithms, either through sheer computational …