94 citations · 217 across the 22 of their papers we have counts for
23 papers
Efficient Memory Tiering in a Virtual Machine
Chandra Prakash, Aravinda Prasad, Sandeep Kumar +1
Memory tiering is the norm to effectively tackle the increasing server memory total cost of ownership (TCO) and the growing data demands of modern data center workloads. However, t…
iMIV: in-Memory Integrity Verification for NVM
Rajat Jain, Aravinda Prasad, Sreenivas Subramoney +1
Non-volatile Memory (NVM) could bridge the gap between memory and storage. However, NVMs are susceptible to data remanence attacks. Thus, multiple security metadata must persist al…
Constable: Improving Performance and Power Efficiency by Safely Eliminating Load Instruction Execution
Rahul Bera, Adithya Ranganathan, Joydeep Rakshit +8
Load instructions often limit instruction-level parallelism (ILP) in modern processors due to data and resource dependences they cause. Prior techniques like Load Value Prediction…
QCQA: Quality and Capacity-aware grouped Query Attention
Vinay Joshi, Prashant Laddha, Shambhavi Sinha +2
Excessive memory requirements of key and value features (KV-cache) present significant challenges in the autoregressive inference of large language models (LLMs), restricting both…
Taming Server Memory TCO with Multiple Software-Defined Compressed Tiers
Sandeep Kumar, Aravinda Prasad, Sreenivas Subramoney
Memory accounts for 33 - 50% of the total cost of ownership (TCO) in modern data centers. We propose a novel solution to tame memory TCO through the novel creation and judicious ma…
CiMNet: Towards Joint Optimization for DNN Architecture and Configuration for Compute-In-Memory Hardware
Souvik Kundu, Anthony Sarah, Vinay Joshi +2
With the recent growth in demand for large-scale deep neural networks, compute in-memory (CiM) has come up as a prominent solution to alleviate bandwidth and on-chip interconnect b…
Telescope: Telemetry at Terabyte Scale
Alan Nair, Sandeep Kumar, Aravinda Prasad +2
Data-hungry applications that require terabytes of memory have become widespread in recent years. To meet the memory needs of these applications, data centers are embracing tiered…
Motivating Next-Generation OS Physical Memory Management for Terabyte-Scale NVMMs
Shivank Garg, Aravinda Prasad, Debadatta Mishra +1
Software managed byte-addressable hybrid memory systems consisting of DRAMs and NVMMs offer a lot of flexibility to design efficient large scale data processing applications. Opera…
Reclaimer: A Reinforcement Learning Approach to Dynamic Resource Allocation for Cloud Microservices
Quintin Fettes, Avinash Karanth, Razvan Bunescu +2
Many cloud applications are migrated from the monolithic model to a microservices framework in which hundreds of loosely-coupled microservices run concurrently, with significant be…
Real analytic functions and monomial curves
János Kollár
We show that a function is real analytic at the origin iff it is arc-analytic, has a subanalytic graph, and its restriction to every monomial curve is analytic. This complements re…
VEGETA: Vertically-Integrated Extensions for Sparse/Dense GEMM Tile Acceleration on CPUs
Geonhwa Jeong, Sana Damani, Abhimanyu Rajeshkumar Bambhaniya +5
Deep Learning (DL) acceleration support in CPUs has recently gained a lot of traction, with several companies (Arm, Intel, IBM) announcing products with specialized matrix engines…
ApHMM: Accelerating Profile Hidden Markov Models for Fast and Energy-Efficient Genome Analysis
Can Firtina, Kamlesh Pillai, Gurpreet S. Kalsi +10
Profile hidden Markov models (pHMMs) are widely employed in various bioinformatics applications to identify similarities between biological sequences, such as DNA or protein sequen…
SeGraM: A Universal Hardware Accelerator for Genomic Sequence-to-Graph and Sequence-to-Sequence Mapping
Damla Senol Cali, Konstantinos Kanellopoulos, Joel Lindegger +15
A critical step of genome sequence analysis is the mapping of sequenced DNA fragments (i.e., reads) collected from an individual to a known linear reference genome sequence (i.e.,…
Unsupervised Learning of Depth, Camera Pose and Optical Flow from Monocular Video
Dipan Mandal, Abhilash Jain
We propose DFPNet -- an unsupervised, joint learning system for monocular Depth, Optical Flow and egomotion (Camera Pose) estimation from monocular image sequences. Due to the natu…
Robust 3D Scene Segmentation through Hierarchical and Learnable Part-Fusion
Anirud Thyagharajan, Benjamin Ummenhofer, Prashant Laddha +2
3D semantic segmentation is a fundamental building block for several scene understanding applications such as autonomous driving, robotics and AR/VR. Several state-of-the-art seman…
RASA: Efficient Register-Aware Systolic Array Matrix Engine for CPU
Geonhwa Jeong, Eric Qin, Ananda Samajdar +4
As AI-based applications become pervasive, CPU vendors are starting to incorporate matrix engines within the datapath to boost efficiency. Systolic arrays have been the premier arc…
Pythia: A Customizable Hardware Prefetching Framework Using Online Reinforcement Learning
Rahul Bera, Konstantinos Kanellopoulos, Anant V. Nori +3
Past research has proposed numerous hardware prefetching techniques, most of which rely on exploiting one specific type of program context information (e.g., program counter, cache…
Page Table Management for Heterogeneous Memory Systems
Sandeep Kumar, Aravinda Prasad, Smruti R. Sarangi +1
Modern enterprise servers are increasingly embracing tiered memory systems with a combination of low latency DRAMs and large capacity but high latency non-volatile main memories (N…
Proximu$: Efficiently Scaling DNN Inference in Multi-core CPUs through Near-Cache Compute
Anant V. Nori, Rahul Bera, Shankar Balachandran +5
Deep Neural Network (DNN) inference is emerging as the fundamental bedrock for a multitude of utilities and services. CPUs continue to scale up their raw compute capabilities for D…
AccSS3D: Accelerator for Spatially Sparse 3D DNNs
Om Ji Omer, Prashant Laddha, Gurpreet S Kalsi +6
Semantic understanding and completion of real world scenes is a foundational primitive of 3D Visual perception widely used in high-level applications such as robotics, medical imag…
GenASM: A High-Performance, Low-Power Approximate String Matching Acceleration Framework for Genome Sequence Analysis
Damla Senol Cali, Gurpreet S. Kalsi, Zülal Bingöl +13
Genome sequence analysis has enabled significant advancements in medical and scientific areas such as personalized medicine, outbreak tracing, and the understanding of evolution. U…
DSPatch: Dual Spatial Pattern Prefetcher
Rahul Bera, Anant V. Nori, Onur Mutlu +1
High main memory latency continues to limit performance of modern high-performance out-of-order cores. While DRAM latency has remained nearly the same over many generations, DRAM b…
MARS: Memory Aware Reordered Source
Ishwar Bhati, Udit Dhawan, Jayesh Gaur +2
Memory bandwidth is critical in today's high performance computing systems. The bandwidth is particularly paramount for GPU workloads such as 3D Gaming, Imaging and Perceptual Comp…