Neural personalized recommendation models are used across a wide variety of datacenter applications including search, social media, and entertainment. State-of-the-art models comprise large embedding tables that have billions of parameters requiring ...

Modern day computing increasingly relies on specialization to satiate growing performance and efficiency requirements. A core challenge in designing such specialized hardware architectures is how to perform mapping space search, i.e., search for an ...

Quantum computing (QC) is a new paradigm offering the potential of exponential speedups over classical computing for certain computational problems. Each additional qubit doubles the size of the computational state space available to a QC algorithm. ...

Due to the unreliability and limited capacity of existing quantum computer prototypes, quantum circuit simulation continues to be a vital tool for validating next generation quantum computers and for studying variational quantum algorithms, which are ...

Processing-using-DRAM has been proposed for a limited set of basic operations (i.e., logic operations, addition). However, in order to enable full adoption of processing-using-DRAM, it is necessary to provide support for more complex operations. In this ...

Current Noisy Intermediate-Scale Quantum (NISQ) computers are useful in developing the quantum computing stack, test quantum algorithms, and establish the feasibility of quantum computing. However, different statistically significant errors permeate ...

Low-resolution uniform quantization (e.g., 4-bit bitwidth) for both Deep Neural Network (DNN) weights and data has emerged as an important technique for efficient inference. Departing from conventional quantization, we describe a novel training approach ...

High spatiotemporal resolution can offer high precision for vision applications, which is particularly useful to capture the nuances of visual features, such as for augmented reality. Unfortunately, capturing and processing high spatiotemporal visual ...

Side-channel attacks such as Spectre rely on properties of modern CPUs that permit discovery of microarchitectural state via timing of various operations. The Weird Machine concept is an increasingly popular model for characterization of emergent ...

Video sharing (e.g., YouTube, Vimeo, Facebook, TikTok) accounts for the majority of internet traffic, and video processing is also foundational to several other key workloads (video conferencing, virtual/augmented reality, cloud gaming, video in ...

Current quantum computers are especially error prone and require high levels of optimization to reduce operation counts and maximize the probability the compiled program will succeed. These computers only support operations decomposed into one- and two-...

The end of Dennard scaling and decline of Moore's law has prompted the proliferation of hardware accelerators for a wide range of application domains. Yet, at the dawn of an era of specialized computing, left behind the trend is the general-purpose ...

While field-programmable gate arrays (FPGAs) have been widely deployed into cloud platforms, the high programming complexity and the inability to manage FPGA resources in an elastic/scalable manner largely limits the adoption of FPGA acceleration. ...

Statistical machine learning often uses probabilistic models and algorithms, such as Markov Chain Monte Carlo (MCMC), to solve a wide range of problems. Probabilistic computations, often considered too slow on conventional processors, can be accelerated ...