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Trapped-Ion (TI) technology offers potential breakthroughs for Noisy Intermediate Scale Quantum (NISQ) computing. TI qubits offer extended coherence times and high gate fidelity, making them appealing for large-scale NISQ computers. Constructing such computers demands a distributed architecture connecting Quantum Charge Coupled Devices (QCCDs) via quantum matter-links and photonic switches. However, current distributed TI NISQ computers face hardware and system challenges. Entangling qubits across a photonic switch introduces significant latency, while existing compilers generate suboptimal mappings due to their unawareness of the interconnection topology. In this paper, we introduce TITAN, a large-scale distributed TI NISQ computer, which employs an innovative photonic interconnection design to reduce entanglement latency and an advanced partitioning and mapping algorithm to optimize matter-link communications. Our evaluations show that TITAN greatly enhances quantum application performance by 56.6% and fidelity by 19.7% compared to existing systems. 

This paper presents \textit{OFHE}, an electro-optical accelerator designed to process Discretized TFHE (DTFHE) operations, which encrypt multi-bit messages and support homomorphic multiplications, lookup table operations and full-domain functional bootstrappings. While DTFHE is more efficient and versatile than other fully homomorphic encryption schemes, it requires 32-, 64-, and 128-bit polynomial multiplications, which can be time-consuming. Existing TFHE accelerators are not easily upgradable to support DTFHE operations due to limited datapaths, a lack of datapath bit-width reconfigurability, and power inefficiencies when processing FFT and inverse FFT (IFFT) kernels. Compared to prior TFHE accelerators, OFHE addresses these challenges by improving the DTFHE operation latency by 8.7\%, the DTFHE operation throughput by $$57\%$$, and the DTFHE operation throughput per Watt by $$94\%$$. 

The carbon footprint associated with large language models (LLMs) is a significant concern, encompassing emissions from their training, inference, experimentation, and storage processes, including operational and embodied carbon emissions. An essential aspect is accurately estimating the carbon impact of emerging LLMs even before their training, which heavily relies on GPU usage. Existing studies have reported the carbon footprint of LLM training, but only one tool, mlco2, can predict the carbon footprint of new neural networks prior to physical training. However, mlco2 has several serious limitations. It cannot extend its estimation to dense or mixture-of-experts (MoE) LLMs, disregards critical architectural parameters, focuses solely on GPUs, and cannot model embodied carbon footprints. Addressing these gaps, we introduce \textit{\carb}, an end-to-end carbon footprint projection model designed for both dense and MoE LLMs. Compared to mlco2, \carb~significantly enhances the accuracy of carbon footprint estimations for various LLMs. The source code is released at \url{https://github.com/SotaroKaneda/MLCarbon}. 

The widespread use of machine learning is changing our daily lives. Unfortunately, clients are often concerned about the privacy of their data when using machine learning-based applications. To address these concerns, the development of privacy-preserving machine learning (PPML) is essential. One promising approach is the use of fully homomorphic encryption (FHE) based PPML, which enables services to be performed on encrypted data without decryption. Although the speed of computationally expensive FHE operations can be significantly boosted by prior ASIC-based FHE accelerators, the performance of key-switching, the dominate primitive in various FHE operations, is seriously limited by their small bit-width datapaths and frequent matrix transpositions. In this paper, we present an electro-optical (EO) PPML accelerator, PriML, to accelerate FHE operations. Its 512-bit datapath supporting 510-bit residues greatly reduces the key-switching cost. We also create an in-scratchpad-memory transpose unit to fast transpose matrices. Compared to prior PPML accelerators, on average, PriML reduces the latency of various machine learning applications by > 94.4% and the energy consumption by > 95%. 

Abstract The exponential growth of information stored in data centers and computational power required for various data-intensive applications, such as deep learning and AI, call for new strategies to improve or move beyond the traditional von Neumann architecture. Recent achievements in information storage and computation in the optical domain, enabling energy-efficient, fast, and high-bandwidth data processing, show great potential for photonics to overcome the von Neumann bottleneck and reduce the energy wasted to Joule heating. Optically readable memories are fundamental in this process, and while light-based storage has traditionally (and commercially) employed free-space optics, recent developments in photonic integrated circuits (PICs) and optical nano-materials have opened the doors to new opportunities on-chip. Photonic memories have yet to rival their electronic digital counterparts in storage density; however, their inherent analog nature and ultrahigh bandwidth make them ideal for unconventional computing strategies. Here, we review emerging nanophotonic devices that possess memory capabilities by elaborating on their tunable mechanisms and evaluating them in terms of scalability and device performance. Moreover, we discuss the progress on large-scale architectures for photonic memory arrays and optical computing primarily based on memory performance. 

Phase change chalcogenides such as Ge₂Sb₂Te₅(GST) have recently enabled advanced optical devices for applications such as in-memory computing, reflective displays, tunable metasurfaces, and reconfigurable photonics. However, designing phase change optical devices with reliable and efficient electrical control is challenging due to the requirements of both high amorphization temperatures and extremely fast quenching rates for reversible switching. Here, we use a Multiphysics simulation framework to model three waveguide-integrated microheaters designed to switch optical phase change materials. We explore the effects of geometry, doping, and electrical pulse parameters to optimize the switching speed and minimize energy consumption in these optical devices. 

A photonic generative adversarial network that harnesses optoelectronic noises to generate handwritten numbers is demonstrated.