Predicting Critical Warps in Near-Threshold GPGPU Applications Using a Dynamic Choke Point Analysis

General purpose graphics processing units (GP-GPU), owing to their enormous thread-level parallelism, can significantly improve the power consumption at the near-threshold (NTC) operating region, while offering close to a super-threshold performance. However, process variation (PV) can drastically reduce the GPU performance at NTC. In this work, choke points—a unique device-level characteristic of PV at NTC—that can exacerbate the warp criticality problem in GPUs have been explored. It is shown that the modern warp schedulers cannot tackle the choke point induced critical warps in an NTC GPU. Additionally, Choke Point Aware Warp Speculator, a circuit-architectural solution is proposed to dynamically predict the critical warps in GPUs, and accelerate them in their respective execution units. The best scheme achieves an average improvement of ∼39% in performance, and ∼31% in energy-efficiency, over one state-of-the-art warp scheduler, across 15 GPGPU applications, while incurring marginal hardware overheads

RAMP-Net: A Robust Adaptive MPC for Quadrotors via Physics-informed Neural Network

Model Predictive Control (MPC) is a state-of-the-art (SOTA) control technique which requires solving hard constrained optimization problems iteratively. For uncertain dynamics, analytical model based robust MPC imposes additional constraints, increasing the hardness of the problem. The problem exacerbates in performance-critical applications, when more compute is required in lesser time. Data-driven regression methods such as Neural Networks have been proposed in the past to approximate system dynamics. However, such models rely on high volumes of labeled data, in the absence of symbolic analytical priors. This incurs non-trivial training overheads. Physics-informed Neural Networks (PINNs) have gained traction for approximating non-linear system of ordinary differential equations (ODEs), with reasonable accuracy. In this work, we propose a Robust Adaptive MPC framework via PINNs (RAMP-Net), which uses a neural network trained partly from simple ODEs and partly from data. A physics loss is used to learn simple ODEs representing ideal dynamics. Having access to analytical functions inside the loss function acts as a regularizer, enforcing robust behavior for parametric uncertainties. On the other hand, a regular data loss is used for adapting to residual disturbances (non-parametric uncertainties), unaccounted during mathematical modelling. Experiments are performed in a simulated environment for trajectory tracking of a quadrotor. We report 7.8% to 43.2% and 8.04% to 61.5% reduction in tracking errors for speeds ranging from 0.5 to 1.75 m/s compared to two SOTA regression based MPC methods

EV-Planner: Energy-Efficient Robot Navigation via Event-Based Physics-Guided Neuromorphic Planner

Vision-based object tracking is an essential precursor to performing autonomous aerial navigation in order to avoid obstacles. Biologically inspired neuromorphic event cameras are emerging as a powerful alternative to frame-based cameras, due to their ability to asynchronously detect varying intensities (even in poor lighting conditions), high dynamic range, and robustness to motion blur. Spiking neural networks (SNNs) have gained traction for processing events asynchronously in an energy-efficient manner. On the other hand, physics-based artificial intelligence (AI) has gained prominence recently, as they enable embedding system knowledge via physical modeling inside traditional analog neural networks (ANNs). In this letter, we present an event-based physics-guided neuromorphic planner (EV-Planner) to perform obstacle avoidance using neuromorphic event cameras and physics-based AI. We consider the task of autonomous drone navigation where the mission is to detect moving gates and fly through them while avoiding a collision. We use event cameras to perform object detection using a shallow spiking neural network in an unsupervised fashion. Utilizing the physical equations of the brushless DC motors present in the drone rotors, we train a lightweight energy-aware physics-guided neural network with depth inputs. This predicts the optimal flight time responsible for generating near-minimum energy paths. We spawn the drone in the Gazebo simulator and implement a sensor-fused vision-to-planning neuro-symbolic framework using Robot Operating System (ROS). Simulation results for safe collision-free flight trajectories are presented with performance analysis and potential future research direction

On the Composition of Randomized Query Complexity and Approximate Degree

For any Boolean functions f and g, the question whether R(f?g) = ??(R(f) ? R(g)), is known as the composition question for the randomized query complexity. Similarly, the composition question for the approximate degree asks whether deg?(f?g) = ??(deg?(f)?deg?(g)). These questions are two of the most important and well-studied problems in the field of analysis of Boolean functions, and yet we are far from answering them satisfactorily. It is known that the measures compose if one assumes various properties of the outer function f (or inner function g). This paper extends the class of outer functions for which R and deg? compose. A recent landmark result (Ben-David and Blais, 2020) showed that R(f?g) = ?(noisyR(f)? R(g)). This implies that composition holds whenever noisyR(f) = ??(R(f)). We show two results: 1. When R(f) = ?(n), then noisyR(f) = ?(R(f)). In other words, composition holds whenever the randomized query complexity of the outer function is full. 2. If R composes with respect to an outer function, then noisyR also composes with respect to the same outer function. On the other hand, no result of the type deg?(f?g) = ?(M(f) ? deg?(g)) (for some non-trivial complexity measure M(?)) was known to the best of our knowledge. We prove that deg?(f?g) = ??(?{bs(f)} ? deg?(g)), where bs(f) is the block sensitivity of f. This implies that deg? composes when deg?(f) is asymptotically equal to ?{bs(f)}. It is already known that both R and deg? compose when the outer function is symmetric. We also extend these results to weaker notions of symmetry with respect to the outer function

On the Composition of Randomized Query Complexity and Approximate Degree

For any Boolean functions $f$ and $g$, the question whether $R(f\circ g) = \tilde{\Theta}(R(f)R(g))$, is known as the composition question for the randomized query complexity. Similarly, the composition question for the approximate degree asks whether $\widetilde{deg}(f\circ g) = \tilde{\Theta}(\widetilde{deg}(f)\cdot\widetilde{deg}(g))$. These questions are two of the most important and well-studied problems, and yet we are far from answering them satisfactorily. It is known that the measures compose if one assumes various properties of the outer function $f$ (or inner function $g$). This paper extends the class of outer functions for which $\text{R}$ and $\widetilde{\text{deg}}$ compose. A recent landmark result (Ben-David and Blais, 2020) showed that $R(f \circ g) = \Omega(noisyR(f)\cdot R(g))$. This implies that composition holds whenever noisyR(f) = \Tilde{\Theta}(R(f)). We show two results: (1)When $R(f) = \Theta(n)$, then $noisyR(f) = \Theta(R(f))$. (2) If $\text{R}$ composes with respect to an outer function, then $\text{noisyR}$ also composes with respect to the same outer function. On the other hand, no result of the type $\widetilde{deg}(f \circ g) = \Omega(M(f) \cdot \widetilde{deg}(g))$ (for some non-trivial complexity measure $M(\cdot)$) was known to the best of our knowledge. We prove that $\widetilde{deg}(f\circ g) = \widetilde{\Omega}(\sqrt{bs(f)} \cdot \widetilde{deg}(g)),$ where $bs(f)$ is the block sensitivity of $f$. This implies that $\widetilde{\text{deg}}$ composes when $\widetilde{\text{deg}}(f)$ is asymptotically equal to $\sqrt{\text{bs}(f)}$. It is already known that both $\text{R}$ and $\widetilde{\text{deg}}$ compose when the outer function is symmetric. We also extend these results to weaker notions of symmetry with respect to the outer function

Clock gate on abort: Towards energy-efficient hardware transactional memory

Transactional Memory (TM) is an emerging technology which promises to make parallel programming easier compared to earlier lock based approaches. However, as with any form of speculation, Transactional Memory too wastes a considerable amount of energy when the speculation goes wrong and transaction aborts. For Transactional Memory this wastage will typically be quite high because programmer will often mark a large portion of the code to be executed transactionally. We are proposing to turn-off a processor dynamically by gating all its clocks, whenever any transaction running in it is aborted. We have described a novel protocol which can be used in the Scalable-TCC like Hardware Transactional Memory systems. Also in the protocol we are proposing a gating-aware contention management policy to set the duration of the clock gating period precisely so that both performance and energy can be improved. With our proposal we got an average 19% savings in the total consumed energy and even an average speed-up of 4%.Peer ReviewedPostprint (published version

The Role of Intestinal Fatty Acid Binding Proteins in Protecting Cells from Fatty Acid Induced Impairment of Mitochondrial Dynamics and Apoptosis

Background/Aims: The conformation, folding and lipid binding properties of the intestinal fatty acid binding proteins (IFABP) have been extensively investigated. In contrast, the functional aspects of these proteins are not understood and matter of debates. In this study, we aim to address the deleterious effects of FA overload on cellular components, particularly mitochondria; and how IFABP helps in combating this stress by restoring the mitochondrial dynamics. Methods: In the present study the functional aspect of IFABP under conditions of lipid stress was studied by a string of extensive in-cell studies; flow cytometry by fluorescence-activated cell sorting (FACS), confocal imaging, western blotting and quantitative real time PCR. We deployed ectopic expression of IFABP in rescuing cells under the condition of lipid stress. Again in order to unveil the mechanistic insights of functional traits, we arrayed extensive computational approaches by means of studying centrality calculations along with protein-protein association and ligand induced cluster dissociation. While addressing its functional importance, we used FCS and in-silico computational analyses, to show the structural distribution and the underlying mechanism of IFABP’s action. Results: Ectopic expression of IFABP in HeLa cells has been found to rescue mitochondrial morphological dynamics and restore membrane potential, partially preventing apoptotic damage induced by the increased FAs. These findings have been further validated in the functionally relevant intestinal Caco-2 cells, where the native expression of IFABP protects mitochondrial morphology from abrogation induced by FA overload. However, this native level expression is insufficient to protect against apoptotic cell death, which is rescued, at least partially in cells overexpressing IFABP. In addition, shRNA mediated IFABP knockdown in Caco-2 cells compromises mitochondrial dynamics and switches on intrinsic apoptotic pathways under FA-induced metabolic stress. Conclusion: To summarize, the present study implicates functional significance of IFABP in controlling ligand-induced damage in mitochondrial dynamics and apoptosis

Tight Chang’s-lemma-type bounds for Boolean functions

Chang’s lemma (Duke Mathematical Journal, 2002) is a classical result in mathematics, with applications spanning across additive combinatorics, combinatorial number theory, analysis of Boolean functions, communication complexity and algorithm design. For a Boolean function f that takes values in {-1, 1} let r(f) denote its Fourier rank (i.e., the dimension of the span of its Fourier support). For each positive threshold t, Chang’s lemma provides a lower bound on δ(f):= Pr[f(x) = -1] in terms of the dimension of the span of its characters with Fourier coefficients of magnitude at least 1/t. In this work we examine the tightness of Chang’s lemma with respect to the following three natural settings of the threshold: the Fourier sparsity of f, denoted k(f), the Fourier max-supp-entropy of f, denoted k′(f), defined to be the maximum value of the reciprocal of the absolute value of a non-zero Fourier coefficient, the Fourier max-rank-entropy of f, denoted k′′(f), defined to be the minimum t such that characters whose coefficients are at least 1/t in magnitude span a r(f)-dimensional space. In this work we prove new lower bounds on δ(f) in terms of the above measures. One of our lower bounds, δ(f) = Ω (r(f)2/(k(f) log2 k(f))), subsumes and refines the previously best known upper bound r(f) = O(pk(f) log k(f)) on r(f) in terms of k(f) by Sanyal (Theory of Computing, 2019). We improve upon this bound and show r(f) = O(pk(f)δ(f) log k(f)). Another lower bound, δ(f) = Ω (r(f)/(k′′(f) log k(f))), is based on our improvement of a bound by Chattopadhyay, Hatami, Lovett and Tal (ITCS, 2019) on the sum of absolute values of level-1 Fourier coefficients in terms of F2-degree. We further show that Chang’s lemma for the above-mentioned choices of the threshold is asymptotically outperformed by our bounds for most settings of the parameters involved. Next, we show that our bounds are tight for a wide range of the parameters involved, by constructing functions witnessing their tightness. All the functions we construct are modifications of the Addressing function, where we replace certain input variables by suitable functions. Our final contribution is to construct Boolean functions f for which our lower bounds asymptotically match δ(f), and for any choice of the threshold t, the lower bound obtained from Chang’s lemma is asymptotically smaller than δ(f). Our results imply more refined deterministic one-way communication complexity upper bounds for XOR functions. Given the wide-ranging application of Chang’s lemma to areas like additive combinatorics, learning theory and communication complexity, we strongly feel that our refinements of Chang’s lemma will find many more applications

An Oligopeptide Transporter of Mycobacterium tuberculosis Regulates Cytokine Release and Apoptosis of Infected Macrophages

Background: The Mycobacterium tuberculosis genome encodes two peptide transporters encoded by Rv3665c-Rv3662c and Rv1280c-Rv1283c. Both belong to the family of ABC transporters containing two nucleotide-binding subunits, two integral membrane proteins and one substrate-binding polypeptide. However, little is known about their functions in M. tuberculosis. Here we report functional characterization of the Rv1280c-Rv1283c-encoded transporter and its substrate-binding polypeptide OppA(MTB). Methodology/Principal Findings: OppA(MTB) was capable of binding the tripeptide glutathione and the nonapeptide bradykinin, indicative of a somewhat broad substrate specificity. Amino acid residues G109, N110, N230, D494 and F496, situated at the interface between domains I and III of OppA, were required for optimal peptide binding. Complementaton of an oppA knockout mutant of M. smegmatis with OppA(MTB) confirmed the role of this transporter in importing glutathione and the importance of the aforesaid amino acid residues in peptide transport. Interestingly, this transporter regulated the ability of M. tuberculosis to lower glutathione levels in infected compared to uninfected macrophages. This ability was partly offset by inactivation of oppD. Concomitantly, inactivation of oppD was associated with lowered levels of methyl glyoxal in infected macrophages and reduced apoptosis-inducing ability of the mutant. The ability to induce the production of the cytokines IL-1 beta, IL-6 and TNF-alpha was also compromised after inactivation of oppD. Conclusions: Taken together, these studies uncover the novel observations that this peptide transporter modulates the innate immune response of macrophages infected with M. tuberculosis

Leprosy drug clofazimine activates peroxisome proliferator-activated receptor-γ and synergizes with imatinib to inhibit chronic myeloid leukemia cells

Leukemia stem cells contribute to drug-resistance and relapse in chronic myeloid leukemia (CML) and BCR-ABL1 inhibitor monotherapy fails to eliminate these cells, thereby necessitating alternate therapeutic strategies for patients CML. The peroxisome proliferator-activated receptor-γ (PPARγ) agonist pioglitazone downregulates signal transducer and activator of transcription 5 (STAT5) and in combination with imatinib induces complete molecular response in imatinib-refractory patients by eroding leukemia stem cells. Thiazolidinediones such as pioglitazone are, however, associated with severe side effects. To identify alternate therapeutic strategies for CML we screened Food and Drug Administration-approved drugs in K562 cells and identified the leprosy drug clofazimine as an inhibitor of viability of these cells. Here we show that clofazimine induced apoptosis of blood mononuclear cells derived from patients with CML, with a particularly robust effect in imatinib-resistant cells. Clofazimine also induced apoptosis of CD34+38− progenitors and quiescent CD34+ cells from CML patients but not of hematopoietic progenitor cells from healthy donors. Mechanistic evaluation revealed that clofazimine, via physical interaction with PPARγ, induced nuclear factor kB-p65 proteasomal degradation, which led to sequential myeloblastoma oncoprotein and peroxiredoxin 1 downregulation and concomitant induction of reactive oxygen species-mediated apoptosis. Clofazimine also suppressed STAT5 expression and consequently downregulated stem cell maintenance factors hypoxia-inducible factor-1α and -2α and Cbp/P300 interacting transactivator with Glu/Asp-rich carboxy-terminal domain 2 (CITED2). Combining imatinib with clofazimine caused a far superior synergy than that with pioglitazone, with clofazimine reducing the half maximal inhibitory concentration (IC50) of imatinib by >4 logs and remarkably eroding quiescent CD34+ cells. In a K562 xenograft study clofazimine and imatinib co-treatment showed more robust efficacy than the individual treatments. We propose clinical evaluation of clofazimine in imatinib-refractory CML