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Encrypted AI using fully homomorphic encryption (FHE) provides strong privacy guarantees; but its slow performance has limited practical deployment. Recent works proposed ASICs to accelerate FHE, but require expensive advanced manufacturing…

We performed an experimental review of current diarization systems for the conversational telephone speech (CTS) domain. In detail, we considered a total of eight different algorithms belonging to clustering-based, end-to-end neural…

General matrix/matrix multiplication (GEMM) is crucial for scientific computing and machine learning. However, the increased scale of the computing platforms raises concerns about hardware and software reliability. In this poster, we…

Sequence alignment is a memory bound computation whose performance in modern systems is limited by the memory bandwidth bottleneck. Processing-in-memory architectures alleviate this bottleneck by providing the memory with computing…

In-host shared memory (IVSHMEM) enables high-throughput, zero-copy communication between virtual machines, but today's implementations lack any security control, allowing any application to eavesdrop or tamper with the IVSHMEM region. This…

Embedded heterogeneous systems-on-chip (SoCs) rely on domain-specific hardware accelerators to improve performance and energy efficiency. In particular, programmable multi-core accelerators feature a cluster of processing elements and…

Embedding vector operations are a key component of modern deep neural network workloads. Unlike matrix operations with deterministic access patterns, embedding vector operations exhibit input data-dependent and non-deterministic memory…

This work compares open-source electronic design automation tools with a commercial environment using three representative integrated circuit blocks in the IHP 130 nm open PDK: a common-mode noise filter, a finite-state machine, and a…

Large language models (LLMs) hold tremendous potential for addressing numerous real-world challenges, yet they typically demand significant computational resources and memory. Deploying LLMs onto a resource-limited hardware device with…

The context window of large language models (LLMs) is rapidly increasing, leading to a huge variance in resource usage between different requests as well as between different phases of the same request. Restricted by static parallelism…

Mass spectrometry (MS) based omics data analysis require significant time and resources. To date, few parallel algorithms have been proposed for deducing peptides from mass spectrometry-based data. However, these parallel algorithms were…

In this paper, we investigate a bistatic multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) system over block-fading channels, focusing on the scenario where the sensing and communication receivers (Rxs) are…

Modern interconnects often have programmable processors in the network interface that can be utilized to offload communication processing from host CPU. In this paper, we explore different schemes to support collective operations at the…

Emerging machine learning (ML) models (e.g., transformers) involve memory pin bandwidth-bound matrix-vector (MV) computation in inference. By avoiding pin crossings, processing in memory (PIM) can improve performance and energy for…

This paper investigates a reconfigurable intelligent surface (RIS)-aided wideband massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system with low-resolution analog-to-digital converters…

Full-duplex spoken dialogue systems (FDSDS) enable more natural human-machine interactions by allowing real-time user interruptions and backchanneling, compared to traditional SDS that rely on turn-taking. However, existing benchmarks lack…

The widespread adoption of Large Language Models (LLMs) has exponentially increased the demand for efficient serving systems. With growing requests and context lengths, key-value (KV)-related operations, including attention computation and…

This paper addresses the problem of data detection for a massive Multiple-Input-Multiple-Output (MIMO) base station which utilizes 1-bit Analog-to-Digital Converters (ADCs) for quantizing the uplink signal. The existing literature on…

The rapid advancement of Large Language Models (LLMs) has driven the need for more efficient serving strategies. In this context, efficiency refers to the proportion of requests that meet their Service Level Objectives (SLOs), particularly…

A good audio codec for live applications such as telecommunication is characterized by three key properties: (1) compression, i.e.\ the bitrate that is required to transmit the signal should be as low as possible; (2) latency, i.e.\…