A Case for Self-Managing DRAM Chips: Improving Performance, Efficiency, Reliability, and Security via Autonomous in-DRAM Maintenance Operations

The memory controller is in charge of managing DRAM maintenance operations (e.g., refresh, RowHammer protection, memory scrubbing) in current DRAM chips. Implementing new maintenance operations often necessitates modifications in the DRAM interface, memory controller, and potentially other system components. Such modifications are only possible with a new DRAM standard, which takes a long time to develop, leading to slow progress in DRAM systems. In this paper, our goal is to 1) ease, and thus accelerate, the process of enabling new DRAM maintenance operations and 2) enable more efficient in-DRAM maintenance operations. Our idea is to set the memory controller free from managing DRAM maintenance. To this end, we propose Self-Managing DRAM (SMD), a new low-cost DRAM architecture that enables implementing new in-DRAM maintenance mechanisms (or modifying old ones) with no further changes in the DRAM interface, memory controller, or other system components. We use SMD to implement new in-DRAM maintenance mechanisms for three use cases: 1) periodic refresh, 2) RowHammer protection, and 3) memory scrubbing. We show that SMD enables easy adoption of efficient maintenance mechanisms that significantly improve the system performance and energy efficiency while providing higher reliability compared to conventional DDR4 DRAM. A combination of SMD-based maintenance mechanisms that perform refresh, RowHammer protection, and memory scrubbing achieve 7.6% speedup and consume 5.2% less DRAM energy on average across 20 memory-intensive four-core workloads. We make SMD source code openly and freely available at [128]

Neurostream: Scalable and Energy Efficient Deep Learning with Smart Memory Cubes

open4siHigh-performance computing systems are moving towards 2.5D and 3D memory hierarchies, based on High Bandwidth Memory (HBM) and Hybrid Memory Cube (HMC) to mitigate the main memory bottlenecks. This trend is also creating new opportunities to revisit near-memory computation. In this paper, we propose a flexible processor-in-memory (PIM) solution for scalable and energy-efficient execution of deep convolutional networks (ConvNets), one of the fastest-growing workloads for servers and high-end embedded systems. Our co-design approach consists of a network of Smart Memory Cubes (modular extensions to the standard HMC) each augmented with a many-core PIM platform called NeuroCluster. NeuroClusters have a modular design based on NeuroStream coprocessors (for Convolution-intensive computations) and general-purpose RISC-V cores. In addition, a DRAM-friendly tiling mechanism and a scalable computation paradigm are presented to efficiently harness this computational capability with a very low programming effort. NeuroCluster occupies only 8 percent of the total logic-base (LoB) die area in a standard HMC and achieves an average performance of 240 GFLOPS for complete execution of full-featured state-of-the-art (SoA) ConvNets within a power budget of 2.5 W. Overall 11 W is consumed in a single SMC device, with 22.5 GFLOPS/W energy-efficiency which is 3.5X better than the best GPU implementations in similar technologies. The minor increase in system-level power and the negligible area increase make our PIM system a cost-effective and energy efficient solution, easily scalable to 955 GFLOPS with a small network of just four SMCs.openAzarkhish, Erfan*; Rossi, Davide; Loi, Igor; Benini, LucaAzarkhish, Erfan*; Rossi, Davide; Loi, Igor; Benini, Luc

Reducing main memory access latency through SDRAM address mapping techniques and access reordering mechanisms

As the performance gap between microprocessors and memory continues to increase, main memory accesses result in long latencies which become a factor limiting system performance. Previous studies show that main memory access streams contain significant localities and SDRAM devices provide parallelism through multiple banks and channels. These locality and parallelism have not been exploited thoroughly by conventional memory controllers. In this thesis, SDRAM address mapping techniques and memory access reordering mechanisms are studied and applied to memory controller design with the goal of reducing observed main memory access latency. The proposed bit-reversal address mapping attempts to distribute main memory accesses evenly in the SDRAM address space to enable bank parallelism. As memory accesses to unique banks are interleaved, the access latencies are partially hidden and therefore reduced. With the consideration of cache conflict misses, bit-reversal address mapping is able to direct potential row conflicts to different banks, further improving the performance. The proposed burst scheduling is a novel access reordering mechanism, which creates bursts by clustering accesses directed to the same rows of the same banks. Subjected to a threshold, reads are allowed to preempt writes and qualified writes are piggybacked at the end of the bursts. A sophisticated access scheduler selects accesses based on priorities and interleaves accesses to maximize the SDRAM data bus utilization. Consequentially burst scheduling reduces row conflict rate, increasing and exploiting the available row locality. Using a revised SimpleScalar and M5 simulator, both techniques are evaluated and compared with existing academic and industrial solutions. With SPEC CPU2000 benchmarks, bit-reversal reduces the execution time by 14% on average over traditional page interleaving address mapping. Burst scheduling also achieves a 15% reduction in execution time over conventional bank in order scheduling. Working constructively together, bit-reversal and burst scheduling successfully achieve a 19% speedup across simulated benchmarks

Master of Science

thesisTo minimize resource consumption and maximize performance, computer architecture research has been investigating approaches that may compute inaccurate solutions. Such hardware inaccuracies may induce a wide variety of program behaviors which are not obs

Monolithically Integrated SRAM-ReRAM Cache-Main Memory System

Emerging non-volatile memories are dense and potentially compatible with standard CMOS processes, enabling a monolithically integrated CPU-main memory chip. However, area constraints impact the feasibility of fitting the entirety of a multi-core CPU and main memory system into a single die. ReRAM presents a unique opportunity in that it can be fabricated in crosspoint subarrays which leave the bulk of transistors beneath them available for other logic. However, ReRAM also poses a performance challenge; the latency is generally much higher than that of DRAM. Compensating for this through the increased bandwidth afforded from being on-die poses an architectural problem. The access circuitry for ReRAM subarrays requires only a small percentage of the area beneath the array. Still, this dense circuitry and wiring disrupts the layouts of irregular logic like CPUs. Caches are very regular and composed of smaller subarrays, making them a better candidate to place beneath crosspoint subarrays. By co-designing the cache subarrays and ReRAM crosspoint subarrays, minimal disruption to the cache logic can be achieved while still covering the bulk of the last-level cache area in ReRAM. This work explores the design space when co-designing the last-level cache and ReRAM crosspoint subarrays. Using a modified version of Cacti, we are able to explore the design trade-offs when integrating ReRAM and cache and quantify the impact the ReRAM has on the last-level cache. This design space exploration gives us a first order approximation of the memory capacity of a monolithic computer and informs architectural simulations of such a machine. We also examine how the physical integration presents opportunities for logical integration of the last-level cache and main memory. The interconnects and controllers can be combined, and the addressing can be such that data movement between the main memory and cache is primarily vertical. These optimizations can result in area and energy savings with minor impacts on performance. The second section of this work explores one architectural style which can balance the monolithic memory system and a general-purpose compute system---a tiled multicore with wide SIMD and multi-threading. We develop a simulator for this architecture capable of simulating a wide variety of system parameters. Through a design space exploration of many of the parameters across sparse, irregular graph kernels and dense, streaming computations, we find monolithic ReRAM exceeds the performance of a state-of-the-art DRAM system for memory intensive workloads given enough parallelism. We further develop an analytic model to describe our system and highlight the important performance characteristics for a monolithic CPU-main memory chip. The analytic model is validated against our simulation data. Using this model, we examine the architectural balance of the systems we simulated. Finally, we develop an RTL model of the combined cache--main memory interface. This gives a more accurate model for the increase in resources required for the combined controller. We additionally develop a system-on-a-chip with an RTL model that alters requests to the FPGA's main memory to be at the speed of ReRAM requests. This model is used to show the performance of more computationally intensive benchmarks. It also is the first step toward creating a test chip for a monolithically integrated ReRAM main memory

Runtime Systems for Persistent Memories

Emerging persistent memory (PM) technologies promise the performance of DRAM with the durability of disk. However, several challenges remain in existing hardware, programming, and software systems that inhibit wide-scale PM adoption. This thesis focuses on building efficient mechanisms that span hardware and operating systems, and programming languages for integrating PMs in future systems. First, this thesis proposes a mechanism to solve low-endurance problem in PMs. PMs suffer from limited write endurance---PM cells can be written only 10^7-10^9 times before they wear out. Without any wear management, PM lifetime might be as low as 1.1 months. This thesis presents Kevlar, an OS-based wear-management technique for PM, that requires no new hardware. Kevlar uses existing virtual memory mechanisms to remap pages, enabling it to perform both wear leveling---shuffling pages in PM to even wear; and wear reduction---transparently migrating heavily written pages to DRAM. Crucially, Kevlar avoids the need for hardware support to track wear at fine grain. It relies on a novel wear-estimation technique that builds upon Intel's Precise Event Based Sampling to approximately track processor cache contents via a software-maintained Bloom filter and estimate write-back rates at fine grain. Second, this thesis proposes a persistency model for high-level languages to enable integration of PMs in to future programming systems. Prior works extend language memory models with a persistency model prescribing semantics for updates to PM. These approaches require high-overhead mechanisms, are restricted to certain synchronization constructs, provide incomplete semantics, and/or may recover to state that cannot arise in fault-free program execution. This thesis argues for persistency semantics that guarantee failure atomicity of synchronization-free regions (SFRs) --- program regions delimited by synchronization operations. The proposed approach provides clear semantics for the PM state that recovery code may observe and extends C++11's "sequential consistency for data-race-free" guarantee to post-failure recovery code. To this end, this thesis investigates two designs for failure-atomic SFRs that vary in performance and the degree to which commit of persistent state may lag execution. Finally, this thesis proposes StrandWeaver, a hardware persistency model that minimally constrains ordering on PM operations. Several language-level persistency models have emerged recently to aid programming recoverable data structures in PM. The language-level persistency models are built upon hardware primitives that impose stricter ordering constraints on PM operations than the persistency models require. StrandWeaver manages PM order within a strand, a logically independent sequence of PM operations within a thread. PM operations that lie on separate strands are unordered and may drain concurrently to PM. StrandWeaver implements primitives under strand persistency to allow programmers to improve concurrency and relax ordering constraints on updates as they drain to PM. Furthermore, StrandWeaver proposes mechanisms that map persistency semantics in high-level language persistency models to the primitives implemented by StrandWeaver.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155100/1/vgogte_1.pd

Verkkoliikenteen hajauttaminen rinnakkaisprosessoitavaksi ohjelmoitavan piirin avulla

The expanding diversity and amount of traffic in the Internet requires increasingly higher performing devices for protecting our networks against malicious activities. The computational load of these devices may be divided over multiple processing nodes operating in parallel to reduce the computation load of a single node. However, this requires a dedicated controller that can distribute the traffic to and from the nodes at wire-speed. This thesis concentrates on the system topologies and on the implementation aspects of the controller. A field-programmable gate array (FPGA) device, based on a reconfigurable logic array, is used for implementation because of its integrated circuit like performance and high-grain programmability. Two hardware implementations were developed; a straightforward design for 1-gigabit Ethernet, and a modular, highly parameterizable design for 10-gigabit Ethernet. The designs were verified by simulations and synthesizable testbenches. The designs were synthesized on different FPGA devices while varying parameters to analyze the achieved performance. High-end FPGA devices, such as Altera Stratix family, met the target processing speed of 10-gigabit Ethernet. The measurements show that the controller's latency is comparable to a typical switch. The results confirm that reconfigurable hardware is the proper platform for low-level network processing where the performance is prioritized over other features. The designed architecture is versatile and adaptable to applications expecting similar characteristics.Internetin edelleen lisääntyvä ja monipuolistuva liikenne vaatii entistä tehokkaampia laitteita suojaamaan tietoliikenneverkkoja tunkeutumisia vastaan. Tietoliikennelaitteiden kuormaa voidaan jakaa rinnakkaisille yksiköille, jolloin yksittäisen laitteen kuorma pienenee. Tämä kuitenkin vaatii erityisen kontrolloijan, joka kykenee hajauttamaan liikennettä yksiköille linjanopeudella. Tämä tutkimus keskittyy em. kontrolloijan järjestelmätopologioiden tutkimiseen sekä kontrolloijan toteuttamiseen ohjelmoitavalla piirillä, kuten kenttäohjelmoitava järjestelmäpiiri (eng. field programmable gate-array, FPGA). Kontrolloijasta tehtiin yksinkertainen toteutus 1-gigabitin Ethernet-verkkoihin sekä modulaarinen ja parametrisoitu toteutus 10-gigabitin Ethernet-verkkoihin. Toteutukset verifioitiin simuloimalla sekä käyttämällä syntetisoituvia testirakenteita. Toteutukset syntetisoitiin eri FPGA-piireille vaihtelemalla samalla myös toteutuksen parametrejä. Tehokkaimmat FPGA-piirit, kuten Altera Stratix -piirit, saavuttivat 10-gigabitin prosessointivaatimukset. Mittaustulokset osoittavat, että kontrollerin vasteaika ei poikkea tavallisesta verkkokytkimestä. Työn tulokset vahvistavat käsitystä, että ohjelmoitavat piirit soveltuvat hyvin verkkoliikenteen matalantason prosessointiin, missä vaaditaan ensisijaisesti suorituskykyä. Suunniteltu arkkitehtuuri on monipuolinen ja soveltuu joustavuutensa ansiosta muihin samantyyppiseen sovelluksiin