Artificial intelligence and machine learning are experiencing widespread adoption in industry and academia. This has been driven by rapid advances in the applications and accuracy of AI through increasingly complex algorithms and models; this, in turn, has spurred research into specialized hardware AI accelerators. Given the rapid pace of advances, it is easy to forget that they are often developed

We present a novel method for neural network quantization. Our method, named UNIQ, emulates a non-uniform k-quantile quantizer and adapts the model to perform well with quantized weights by injecting noise to the weights at training time. As a by-product of injecting noise to weights, we find that activations can also be quantized to as low as 8-bit with only a minor accuracy degradation. Our non-uniform

Unikernel specializes a minimalistic LibOS and a target application into a standalone single-purpose virtual machine (VM) running on a hypervisor, which is referred to as (virtual) appliance. Compared to traditional VMs, Unikernel appliances have smaller memory footprint and lower overhead while guaranteeing the same level of isolation. On the downside, Unikernel strips off the process abstraction

Graphics Processing Units (GPUs) employ large register files to accommodate all active threads and accelerate context switching. Unfortunately, register files are a scalability bottleneck for future GPUs due to long access latency, high power consumption, and large silicon area provisioning. Prior work proposes hierarchical register file to reduce the register file power consumption by caching registers

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Log-Structured Merge Key-Value stores (LSM KVs) are designed to offer good write performance, by capturing client writes in memory, and only later flushing them to storage. Writes are later compacted into a tree-like data structure on disk to improve read performance and to reduce storage space use. It has been widely documented that compactions severely hamper throughput. Various optimizations have

A large class of IoT applications read sensors, execute application logic, and actuate actuators. However, the lack of high-level programming abstractions compromises correctness, especially in the presence of failures and unwanted interleaving between applications. A key problem arises when operations on IoT devices or the application itself fails, which leads to inconsistencies between the physical

System-level Dynamic Binary Translation (DBT) provides the capability to boot an Operating System (OS) and execute programs compiled for an Instruction Set Architecture (ISA) different from that of the host machine. Due to their performance-critical nature, system-level DBT frameworks are typically hand-coded and heavily optimized, both for their guest and host architectures. While this results in

No abstract available.

Atomic context is an execution state of the Linux kernel in which kernel code monopolizes a CPU core. In this state, the Linux kernel may only perform operations that cannot sleep, as otherwise a system hang or crash may occur. We refer to this kind of concurrency bug as a sleep-in-atomic-context (SAC) bug. In practice, SAC bugs are hard to find, as they do not cause problems in all executions. In