We present RQFTL, a demand-based FTL for mobile storage controllers that boosts the effective Logical-To-Physical (L2P) address translation cache capacity over state-of-the-art techniques. RQFTL stores a large part of the L2P cache in a compressed form, and employs a learned data structure called RQRMI that leverages tiny neural nets to quickly find the correct translation entry in the cache. RQFTL uses neural network inference for cache lookups, and rapidly retrains the neural nets to efficiently handle L2P cache updates. It is specifically optimized to achieve high coverage for scattered read accesses, making it suitable for popular read-skewed workloads such as mobile gaming.

Finding latent structures in data is drawing increasing attention in diverse fields such as image and signal processing, fluid dynamics, and machine learning. In this work we examine the problem of finding the main modes of gradient flows. Gradient descent is a fundamental process in optimization where its stochastic version is prominent in training of neural networks. Here our aim is to establish a consistent theory for gradient flows $\boldsymbol{\psi}_t = P(\boldsymbol{\psi})$, where $P$ is a nonlinear homogeneous operator. Our proposed framework stems from analytic solutions of homogeneous flows, previously formalized by Cohen and Gilboa, where the initial condition $\boldmath{\psi}_0$ admits the nonlinear eigenvalue problem $P(\boldsymbol{\psi}_0)=\lambda \boldsymbol{\psi}_0 $. We first present an analytic solution for dynamic mode decomposition (DMD) in such cases. We show an inherent flaw of DMD, which is unable to recover the essential dynamics of the flow. It is evident that DMD is best suited for homogeneous flows of degree one. We propose an adaptive time sampling scheme and show its dynamics are analogue to homogeneous flows of degree one with a fixed step size. Moreover, we adapt DMD to yield a real spectrum, using symmetric matrices. Our analytic solution of the proposed scheme recovers the dynamics perfectly and yields zero error. We then proceed to show the relation between the orthogonal modes $\{\phi_i\}$ and their decay profiles under the gradient flow. We formulate orthogonal nonlinear spectral decomposition (OrthoNS), which recovers the essential latent structures of the gradient descent process. Definitions for spectrum and filtering are given, and a Parseval-type identity is shown. Experimental results on images show the resemblance to direct computations of nonlinear spectral decomposition. A significant speedup (by about two orders of magnitude) is achieved for this application using the proposed method.

Abstract Mobile devices are equipped with increasingly smart batteries designed to provide responsiveness and extended lifetime. However, such smart batteries may present a threat to users’ privacy. We demonstrate that the phone’s power trace sampled from the battery at 1KHz holds enough information to recover a variety of sensitive information. We show techniques to infer characters typed on a touchscreen; to accurately recover browsing history in an open-world setup; and to reliably detect incoming calls, and the photo shots including their lighting conditions. Combined with a novel exfiltration technique that establishes a covert channel from the battery to a remote server via a web browser, these attacks turn the malicious battery into a stealthy surveillance device. We deconstruct the attack by analyzing its robustness to sampling rate and execution conditions. To find mitigations we identify the sources of the information leakage exploited by the attack. We discover that the GPU or DRAM power traces alone are sufficient to distinguish between different websites. However, the CPU and power-hungry peripherals such as a touchscreen are the primary sources of fine-grain information leakage. We consider and evaluate possible mitigation mechanisms, highlighting the challenges to defend against the attacks. In summary, our work shows the feasibility of the malicious battery and motivates further research into system and application-level defenses to fully mitigate this emerging threat.

Intel Software Guard extensions (SGX) enable secure and trusted execution of user code in an isolated enclave to protect against a powerful adversary. Unfortunately, running I/O-intensive, memory-demanding server applications in enclaves leads to significant performance degradation. Such applications put a substantial load on the in-enclave system call and secure paging mechanisms, which turn out to be the main reason for the application slowdown. In addition to the high direct cost of thousands-of-cycles long SGX management instructions, these mechanisms incur the high indirect cost of enclave exits due to associated TLB flushes and processor state pollution.