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AI chips commonly employ SRAM memory as buffers for their reliability and speed, which contribute to high performance. However, SRAM is expensive and demands significant area and energy consumption. Previous studies have explored replacing…
Modern machine learning applications require huge artificial networks demanding in computational power and memory. Light-based platforms promise ultra-fast and energy-efficient hardware, which may help in realizing next-generation data…
Deep networks are now able to achieve human-level performance on a broad spectrum of recognition tasks. Independently, neuromorphic computing has now demonstrated unprecedented energy-efficiency through a new chip architecture based on…
Neural networks have enabled applications in artificial intelligence through machine learning, and neuromorphic computing. Software implementations of neural networks on conventional computers that have separate memory and processor (and…
For 5G it will be important to leverage the available millimeter wave spectrum. To achieve an approximately omni- directional coverage with a similar effective antenna aperture compared to state of the art cellular systems, an antenna array…
Recent advances in photonic integration have propelled microwave photonic technologies to new heights. The ability to interface hybrid material platforms to enhance light-matter interactions has led to the developments of ultra-small and…
Terahertz (THz) communication has emerged as a key enabler for sixth-generation (6G) networks, offering ultrawide bandwidths to support data-intensive applications such as holographic telepresence and immersive extended reality. Recent…
Optical interconnection networks, as enabled by recent advances in silicon photonic device and fabrication technology, have the potential to address on-chip and off-chip communication bottlenecks in many-core systems. Although several…
Photonic data routing in optical networks overcomes the limitations of electronic routers with respect to data rate, latency, and energy consumption while suffering from dynamic power consumption, non-simultaneous usage of multiple…
The rapid emergence of Large Language Models (LLMs) has catalyzed Agentic artificial intelligence (AI), autonomous systems integrating perception, reasoning, and action into closed-loop pipelines for continuous adaptation. While unlocking…
Deep convolutional neural networks (CNN) are widely used in modern artificial intelligence (AI) and smart vision systems but also limited by computation latency, throughput, and energy efficiency on a resource-limited scenario, such as…
The growing demands of artificial intelligence and immersive media require communication beyond bit-level accuracy to meaning awareness. Conventional optical systems that focused on syntactic precision suffer significant inefficiencies.…
Meeting the escalating demands of data transmission and computing, driven by artificial intelligence (AI), requires not only faster optical transceivers but also advanced integration technologies that can seamlessly combine photonic and…
Silicon photonics promises to address the challenges for next-generation short-reach optical interconnects. Growing bandwidth demand in hyper-scale data centers and high-performance computing motivates the development of faster and…
The rapid evolution of forthcoming sixth-generation (6G) wireless networks necessitates the seamless integration of artificial intelligence (AI) with wireless communications to support emerging intelligent applications that demand both…
Significant investments to upgrade and construct large-scale scientific facilities demand commensurate investments in R&D to design algorithms and computing approaches to enable scientific and engineering breakthroughs in the big data era.…
On-chip communication infrastructure is a central component of modern systems-on-chip (SoCs), and it continues to gain importance as the number of cores, the heterogeneity of components, and the on-chip and off-chip bandwidth continue to…
High-performance computing underpins modern artificial intelligence (AI), enabling foundation models, real-time inference and perception in autonomous systems, and data-intensive scientific simulations. Recent advances in quantization…
We expect that many-core microprocessors will push performance per chip from the 10 gigaflop to the 10 teraflop range in the coming decade. To support this increased performance, memory and inter-core bandwidths will also have to scale by…
The availability of unprecedented unsupervised training data, along with neural scaling laws, has resulted in an unprecedented surge in model size and compute requirements for serving/training LLMs. However, the main performance bottleneck…