3D occupancy prediction has recently emerged as a new paradigm for holistic 3D scene understanding and provides valuable information for downstream planning in autonomous driving. Most existing methods, however, are computationally expensive, requiring costly attention-based 2D- 3D transformation and 3D feature processing. In this paper, we present a novel 3D occupancy prediction approach, H30, which features highly efficient architecture designs that incur a significantly lower computational cost as compared to the current state-of-the-art methods. In addition, to compensate for the ambiguity in ground-truth 3D occupancy labels, we advocate leveraging auxiliary tasks to complement the direct 3D supervision. In particular, we integrate multi-camera depth estimation, semantic segmentation, and surface normal estimation via differentiable volume rendering, supervised by corresponding 2D labels that introduces rich and heterogeneous supervision signals. We conduct extensive experiments on the Occ3D-nuScenes and SemanticKITTI benchmarks that demonstrate the superiority of our proposed H30.

Occupancy prediction infers fine-grained 3D geometry and semantics from camera images of the surrounding environment, making it a critical perception task for autonomous driving. Existing methods either adopt dense grids as scene representation which is difficult to scale to high resolution, or learn the entire scene using a single set of sparse queries, which is insufficient to handle the various object characteristics. In this paper, we present ODG, a hierarchical dual sparse Gaussian representation to effectively capture complex scene dynamics. Building upon the observation that driving scenes can be universally decomposed into static and dynamic counterparts, we define dual Gaussian queries to better model the diverse scene objects. We utilize a hierarchical Gaussian transformer to predict the occupied voxel centers and semantic classes along with the Gaussian parameters. Leveraging the real-time rendering capability of 3D Gaussian Splatting, we also impose rendering supervision with available depth and semantic map annotations injecting pixel-level alignment to boost occupancy learning. Extensive experiments on the Occ3D-nuScenes and Occ3D-Waymo benchmarks demonstrate our proposed method sets new state-of-the-art results while maintaining low inference cost.

Networks-on-Chip (NoCs) in chip multiprocessors are prone to within-die process variation as they span the whole chip. To tolerate variation, their voltages (Vdd) carry over-provisioned guardbands. As a result, prior work has proposed to save energy by operating at reduced Vddwhile occasionally suffering and fixing errors. Unfortunately, these proposals use heuristic controller designs that provide no error bounds guarantees. In this work, we develop a scheme that dynamically minimizes the Vddof groups of routers in a variation-prone NoC using formal control-theoretic methods. The scheme, called Sthira, saves substantial energy while guaranteeing the stability and convergence of error rates. We also enhance the scheme with a low-cost secondary network that retransmits erroneous packets for higher energy efficiency. The enhanced scheme is called Sthira+. We evaluate Sthira and Sthira+ with simulations of NoCs with 64-100 routers. In an NoC with 8 routers per Vdddomain, our schemes reduce the average energy consumptionof the NoC by 27%; in a futuristic NoC with one router per Vdd domain, Sthira+ and Sthira reduce the average energy consumption by 36% and 32%, respectively. The performance impact is negligible. These are significant savings over the state-of-the-art. We conclude that formal control is essential, and that the cheaper Sthira is more cost-effective than Sthira+.

As processors seek more resource efficiency, they increasingly need to target multiple goals at the same time, such as a level of performance, power consumption, and average utilization. Robust control solutions cannot come from heuristic-based controllers or even from formal approaches that combine multiple single-parameter controllers. Such controllers may end-up working against each other. What is needed is control-theoretical MIMO (multiple input, multiple output) controllers, which actuate on multiple inputs and control multiple outputs in a coordinated manner. In this paper, we use MIMO control-theory techniques to develop controllers to dynamically tune architectural parameters in processors. To our knowledge, this is the first work in this area. We discuss three ways in which a MIMO controller can be used. We develop an example of MIMO controller and show that it is substantially more effective than controllers based on heuristics or built by combining single-parameter formal controllers. The general approach discussed here is likely to be increasingly relevant as future processors become more resource-constrained and adaptive.

EDRAM cells require periodic refresh, which ends up consuming substantial energy for large last-level caches. In practice, it is well known that different eDRAM cells can exhibit very different charge-retention properties. Unfortunately, current systems pessimistically assume worst-case retention times, and end up refreshing all the cells at a conservatively-high rate. In this paper, we propose an alternative approach. We use known facts about the factors that determine the retention properties of cells to build a new model of eDRAM retention times. The model is called Mosaic. The model shows that the retention times of cells in large eDRAM modules exhibit spatial correlation. Therefore, we logically divide the eDRAM module into regions or tiles, profile the retention properties of each tile, and program their refresh requirements in small counters in the cache controller. With this architecture, also called Mosaic, we refresh each tile at a different rate. The result is a 20x reduction in the number of refreshes in large eDRAM modules - practically eliminating refresh as a source of energy consumption.

On-chip networks are especially vulnerable to within-die parameter variations. Since they connect distant parts of the chip, they need to be designed to work under the most unfavorable parameter values in the chip. This results in energy-inefficient designs. To improve the energy efficiency of on-chip networks, this paper presents a novel approach that relies on monitoring the errors of messages as they traverse the network. Based on the observed errors of messages, the system dynamically decreases or increases the voltage (Vdd) of groups of network routers. With this approach, called Tangle, the different Vddvalues applied to different groups of network routers progressively converge to their lowest, variation-aware, error-free values - always keeping the network frequency unchanged. This saves substantial network energy. In a simulated 64-router network with 4 Vdddomains, Tangle reduces the network energy consumption by an average of 22% with negligible performance impact. In a future network design with one Vdddomain per router, Tangle lowers the network Vddby an average of 21%, reducing the network energy consumption by an average of 28% with negligible performance impact.

As manycores use dynamic energy ever more efficiently, static power consumption becomes a major concern. In particular, in a large manycore running at a low voltage, leakage in on-chip memory modules contributes substantially to the chip's power draw. This is unfortunate, given that, intuitively, the large multi-level cache hierarchy of a manycore is likely to contain a lot of useless data. An effective way to reduce this problem is to use a low-leakage technology such as embedded DRAM (eDRAM). However, eDRAM requires refresh. In this paper, we examine the opportunity of minimizing on-chip memory power further by intelligently refreshing on-chip eDRAM. We present Refrint, a simple approach to perform fine-grained, intelligent refresh of on-chip eDRAM multiprocessor cache hierarchies. We introduce the Refrint algorithms and microarchitecture. We evaluate Refrint in a simulated manycore running 16-threaded parallel applications. We show that an eDRAM-based memory hierarchy with Refrint consumes only 30% of the energy of a conventional SRAM-based memory hierarchy, and induces a slowdown of only 6%. In contrast, an eDRAM-based memory hierarchy without Refrint consumes 56% of the energy of the conventional memory hierarchy, inducing a slowdown of 25%.

Power dissipation limits combined with increased silicon integration have led microprocessor vendors to design chip multiprocessors (CMPs) with relatively simple (lightweight) cores. While these designs provide high throughput, single-thread performance has stagnated or even worsened. Asymmetric CMPs offer some relief by providing a small number of high-performance (aggressive) cores that can accelerate specific threads. However, threads are only accelerated when they can be mapped to an aggressive core, which are restricted in number due to power and thermal budgets of the chip. Rather than using the aggressive cores to accelerate threads, this paper argues that the aggressive cores can have a multiplicative impact on single-thread performance by accelerating a large number of lightweight cores and providing an illusion of a chip full of aggressive cores. Specifically, we propose an adaptive asymmetric CMP, Illusionist, that can dynamically boost the system throughput and get a higher single-thread performance across the chip. To accelerate the performance of many lightweight cores, those few aggressive cores run all the threads that are running on the lightweight cores and generate execution hints. These hints are then used to accelerate the execution of the lightweight cores. However, the hardware resources of the aggressive core are not large enough to allow the simultaneous execution of a large number of threads. To overcome this hurdle, Illusionist performs aggressive dynamic program distillation to execute small, critical segments of each lightweight-core thread. A combination of dynamic code removal and phase-based pruning distill programs to a tiny fraction of their original contents. Experiments demonstrate that Illusionist achieves 35% higher single thread performance for all the threads running on the system, compared to a CMP with all lightweight cores, while achieving almost 2X higher system throughput compared to a CMP with all aggressive cores.

Extreme technology integration in the sub-micron regime comes with a rapid rise in heat dissipation and power density for modern processors. Dynamic voltage scaling is a widely used technique to tackle this problem when high performance is not the main concern. However, the minimum achievable supply voltage for the processor is often bounded by the large on-chip caches since SRAM cells fail at a significantly faster rate than logic cells when reducing supply voltage. This is mainly due to the higher susceptibility of the SRAM structures to process-induced parameter variations. In this work, we propose a highly flexible fault-tolerant cache design, Archipelago, that by reconfiguring its internal organization can efficiently tolerate the large number of SRAM failures that arise when operating in the near-threshold region. Archipelago partitions the cache to multiple autonomous islands with various sizes which can operate correctly without borrowing redundancy from each other. Our configuration algorithm - an adapted version of minimum clique covering - exploits the high degree of flexibility in the Archipelago architecture to reduce the granularity of redundancy replacement and minimize the amount of space lost in the cache when operating in near-threshold region. Using our approach, the operational voltage of a processor can be reduced to 375mV, which translates to 79% dynamic and 51% leakage power savings (in 90nm) for a microprocessor similar to the Alpha 21364. These power savings come with a 4.6% performance drop-off when operating in low power mode and 2% area overhead for the microprocessor.

To meet an insatiable consumer demand for greater performance at less power, silicon technology has scaled to unprecedented dimensions. However, the pursuit of faster processors and longer battery life has come at the cost of reliability. Given the rise of processor reliability as a first-order design constraint, there has been a growing interest in low-cost, non-intrusive techniques for transient fault detection. Many of these recent proposals have counted on the availability of hardware recovery mechanisms. Although common in aggressive out-of-order cores, hardware support for speculative rollback and recovery is less common in lower-end commodity processors. This paper presents Encore, a software-based fault recovery mechanism tailored for these lower-cost systems that lack native hardware support for speculative rollback recovery. Encore combines program analysis, profile data, and simple code transformations to create statistically idempotent code regions that can recover from faults at very little cost. Using this software-only, compiler-based approach, Encore provides the ability to recover from transient faults without specialized hardware or the costs of traditional, full-system checkpointing solutions. Experimental results show that Encore, with just 14% of runtime overhead, can safely recover, on average from 97% of transient faults when coupled with existing detection schemes.

Technology scaling has delivered on its promises of increasing device density on a single chip. However, the voltage scaling trend has failed to keep up, introducing tight power constraints on manufactured parts. In such a scenario, there is a need to incorporate energy-efficient processing resources that can enable more computation within the same power budget. Energy efficiency solutions in the past have typically relied on application specific hardware and accelerators. Unfortunately, these approaches do not extend to general purpose applications due to their irregular and diverse code base. Towards this end, we propose BERET, an energy-efficient co-processor that can be configured to benefit a wide range of applications. Our approach identifies recurring instruction sequences as phases of "temporal regularity" in a program's execution, and maps suitable ones to the BERET hardware, a three-stage pipeline with a bundled execution model. This judicious off-loading of program execution to a reduced-complexity hardware demonstrates significant savings on instruction fetch, decode and register file accesses energy. On average, BERET reduces energy consumption by a factor of 3-4X for the program regions selected across a range of general-purpose and media applications. The average energy savings for the entire application run was 35% over a single-issue in-order processor.

Aggressive technology scaling to 45 nm and below introduces serious reliability challenges to the design of microprocessors. Since a large fraction of chip area is devoted to on-chip caches, it is important to protect these SRAM structures against lifetime and manufacture-time failures. Designers typically overprovision caches with additional resources to overcome hard faults. However, static allocation and binding of redundant spares results in low utilization of the extra resources and ultimately limits the number of defects that can be tolerated. This work re-examines the design of process-variation-tolerant on-chip caches with a focus on providing the flexibility and dynamic reconfigurability necessary to tolerate large numbers of defects with modest hardware overhead. Our approach, ZerehCache, virtually reorganizes the cache data array using a permutation network to provide more degrees of freedom for spare allocation. A graph coloring algorithm is used to configure the network and identify the proper mapping of replacement elements. We perform an extensive design space exploration of both L1/L2 caches to identify several Pareto-optimal ZerehCaches. Given these optimal design points, we employ ZerehCache to extend the effective lifetime of the on-chip caches and prevent early lifetime failures. Finally, yield analysis studies performed on a population of 1,000 chips at the 45 nm technology node demonstrated that an L1 design with 16 percent overhead and an L2 design with eight percent area overhead achieve yields of 99 percent and 96 percent, respectively.

CMOS scaling has long been a source of dramatic performance gains. However, semiconductor feature size reduction has resulted in increasing levels of operating temperatures and current densities. Given that most wearout mechanisms are highly dependent on these parameters, significantly higher failure rates are projected for future technology generations. Consequently, fault tolerance, which has traditionally been a subject of interest for high-end server markets, is now getting emphasis in the mainstream computing systems space. The popular solution for this has been the use of redundancy at a coarse granularity, such as dual/triple modular redundancy. In this work, we challenge the practice of coarse-granularity redundancy by identifying its inability to scale to high failure rate scenarios and investigating the advantages of finer-grained configurations. To this end, this paper presents and evaluates a highly reconfigurable CMP architecture, named as StageNet (SN), that is designed with reliability as its first-class design criteria. SN relies on a reconfigurable network of replicated processor pipeline stages to maximize the useful lifetime of a chip, gracefully degrading performance toward the end of life. Our results show that the proposed SN architecture can perform 40 percent more cumulative work compared to a traditional CMP over 12 years of its lifetime.

Single-thread performance, power efficiency and reliability are critical design challenges of future multicore systems. Although point solutions have been proposed to address these issues, a more fundamental change to the fabric of multicore systems is necessary to seamlessly combat these challenges. Towards this end, this paper proposes CoreGenesis, a dynamically adaptive multiprocessor fabric that blurs out individual core boundaries, and encourages resource sharing across cores for performance, reliability and customized processing. Further, as a manifestation of this vision, the paper provides details of a unified performance-reliability solution that can assemble variable-width processors from a network of (potentially broken) pipeline stage-level resources.

Aggressive technology scaling provides designers with an ever increasing budget of cheaper and faster transistors. Unfortunately, this trend is accompanied by a decline in individual device reliability as transistors become increasingly susceptible to soft errors. We are quickly approaching a new era where resilience to soft errors is no longer a luxury that can be reserved for just processors in high-reliability, mission-critical domains. Even processors used in mainstream computing will soon require protection. However, due to tighter profit margins, reliable operation for these devices must come at little or no cost. This paper presents Shoestring, a minimally invasive software solution that provides high soft error coverage with very little overhead, enabling its deployment even in commodity processors with "shoestring" reliability budgets. Leveraging intelligent analysis at compile time, and exploiting low-cost, symptom-based error detection, Shoestring is able to focus its efforts on protecting statistically-vulnerable portions of program code. Shoestring effectively applies instruction duplication to protect only those segments of code that, when subjected to a soft error, are likely to result in user-visible faults without first exhibiting symptomatic behavior. Shoestring is able to recover from an additional 33.9% of soft errors that are undetected by a symptom-only approach, achieving an overall user-visible failure rate of 1.6%. This reliability improvement comes at a modest performance overhead of 15.8%.

Manufacture-time process variation and life-time failure projections have become a major industry concern. Consequently, fault tolerance, historically of interest only for mission-critical systems, is now gaining attention in the mainstream computing space. Traditionally reliability issues have been addressed at a coarse granularity, e.g., by disabling faulty cores in chip multiprocessors. However, this is not scalable to higher failure rates. In this paper, we propose StageWeb, a fine-grained wearout and variation tolerance solution, that employs a reconfigurable web of replicated processor pipeline stages to construct dependable many-core chips. The interconnection flexibility of StageWeb simultaneously tackles wearout failures (by isolating broken stages) and process variation (by selectively disabling slower stages). Our experiments show that through its wearout tolerance, a StageWeb chip performs up to 70% more cumulative work than a comparable chip multiprocessor. Further, variation mitigation in StageWeb enables it to scale supply voltage more aggressively, resulting in up to 16% energy savings.

Aggressive technology scaling into the nanometer regime has led to a host of reliability challenges in the last several years. Unlike on-chip caches, which can be efficiently protected using conventional schemes, the general core area is less homogeneous and structured, making tolerating defects a much more challenging problem. Due to the lack of effective solutions, disabling non-functional cores is a common practice in industry to enhance manufacturing yield, which results in a significant reduction in system throughput. Although a faulty core cannot be trusted to correctly execute programs, we observe in this work that for most defects, when starting from a valid architectural state, execution traces on a defective core actually coarsely resemble those of fault-free executions. In light of this insight, we propose a robust and heterogeneous core coupling execution scheme, Necromancer, that exploits a functionally dead core to improve system throughput by supplying hints regarding high-level program behavior. We partition the cores in a conventional CMP system into multiple groups in which each group shares a lightweight core that can be substantially accelerated using these execution hints from a potentially dead core. To prevent this undead core from wandering too far from the correct path of execution, we dynamically resynchronize architectural state with the lightweight core. For a 4-core CMP system, on average, our approach enables the coupled core to achieve 78.5% of the performance of a fully functioning core. This defect tolerance and throughput enhancement comes at modest area and power overheads of 5.3% and 8.5%, respectively.

Single-thread performance, reliability and power efficiency are critical design challenges of future multicore systems. Although point solutions have been proposed to address these issues, a more fundamental change to the fabric of multicore systems is necessary to seamlessly combat these challenges. Towards this end, this paper proposes CoreGenesis, a dynamically adaptive multiprocessor fabric that blurs out individual core boundaries, and encourages resource sharing across cores for performance, fault tolerance and customized processing. Further, as a manifestation of this vision, the paper provides details of a unified performance-reliability solution that can assemble variable-width processors from a network of (potentially broken) pipeline stage-level resources. This design relies on interconnection flexibility, microarchitectural innovations, and compiler directed instruction steering, to merge pipeline resources for high single-thread performance. The same flexibility enables it to route around broken components, achieving sub-core level defect isolation. Together, the resulting fabric consists of a pool of pipeline stage-level resources that can be fluidly allocated for accelerating single-thread performance, throughput computing, or tolerating failures.

With growing semiconductor integration, the reliability of individual transistors is expected to rapidly decline in future technology generations. In such a scenario, processors would need to be equipped with fault tolerance mechanisms to tolerate in-field silicon defects. Periodic online testing is a popular technique to detect such failures; however, it tends to impose a heavy testing penalty. In this paper, we propose an adaptive online testing framework to significantly reduce the testing overhead. The proposed approach is unique in its ability to assess the hardware health and apply suitably detailed tests. Thus, a significant chunk of the testing time can be saved for the healthy components. We further extend the framework to work with the StageNet CMP fabric, which provides the flexibility to group together pipeline stages with similar health conditions, thereby reducing the overall testing burden. For a modest 2.6% sensor area overhead, the proposed scheme was able to achieve an 80% reduction in software test instructions over the lifetime of a 16-core CMP.

Extreme technology integration in the sub-micron regime comes with a rapid rise in heat dissipation and power density for modern processors. Dynamic voltage scaling is a widely used technique to tackle this problem when high performance is not needed. However, the minimum achievable supply voltage is often bounded by SRAM cells since they fail at a faster rate than logic cells. In this work, we propose a novel fault-tolerant cache architecture, that by reconfiguring its internal organization can efficiently tolerate SRAM failures that arise when operating in the ultra low voltage region. Using our approach, the operational voltage of a processor can be reduced to 420mV, which translates to 80% dynamic and 73% leakage power savings in 90nm.

Aggressive technology scaling to 45nm and below introduces serious reliability challenges to the design of microprocessors. Large SRAM structures used for caches are particularly sensitive to process variation due to their high density and organization. Designers typically over-provision caches with additional resources to overcome the hard-faults. However, static allocation and binding of redundant resources results in low utilization of the extra resources and ultimately limits the number of defects that can be tolerated. This work re-examines the design of process variation tolerant on-chip caches with the focus on flexibility and dynamic reconfigurability to allow a large number defects to be tolerated with modest hardware overhead. Our approach, ZerehCache, combines redundant data array elements with a permutation network for providing a higher degree of freedom on replacement. A graph coloring algorithm is used to configure the network and find the proper mapping of replacement elements. We perform an extensive design space exploration of both L1/L2 caches to identify several Pareto optimal ZerehCaches. For the yield analysis, a population of 1000 chips was studied at the 45nm technology node; L1 designs with 16% and an L2 designs with 8% area overheads achieve yields of 99% and 96%, respectively.

Although CMOS feature size scaling has been the source of dramatic performance gains, it has lead to mounting reliability concerns due to increasing power densities and on-chip temperatures. Given that most wearout mechanisms that plague semiconductor devices are highly dependent on these parameters, significantly higher failure rates are projected for future technology generations. Traditional techniques for dealing with device failures have relied on coarse-grained redundancy to maintain service in the face of failed components. In this work, we challenge this practice by identifying its inability to scale to high failure rate scenarios and investigate the advantages of finer-grained configurations. We use this study to motivate the design of StageNet, an embedded CMP architecture designed from its inception with reliability as a first class design constraint. StageNet relies on a reconfigurable network of replicated processor pipeline stages to maximize the useful lifetime of the chip, gracefully degrading performance toward end of life. This paper addresses the microarchitecture of the basic building block of StageNet, named StageNetSlice, which is a processor core comprised of networked pipeline stages. A naive slice design results in approximately 4X slowdown verses a traditional processor due to longer communication delays in the pipeline. However, several small design changes that eliminate inter-stage communication paths and minimize communication bandwidth reduce this overhead to 11% on average while providing high levels of fine-grain adaptability.

The poster presents a novel and flexible architecture for deinterleaving combined pulse trains. Deinterleaving an interlaced pulse train arises in various areas of signal processing applications. Most of these applications require the identification of the main characteristics of pulse trains such as frequency. So far, most of the suggested algorithms for solving this problem are restricted to problems with several limiting assumptions. However, our design solves deinterleaving problem in a more general case by considering all the disturbing conditions such as jitter, dropped pulses, arbitrary start and end points, and a few number of existing pulses from one pulse train available. We used a modified version of sequential search for designing a parallel solution for deinterleaving problem. The proposed architecture utilized parallelism in order to achieve the best possible combination of accuracy and performance. This architecture employs several parameters in order to gain the desirable combination of accuracy, speed, memory usage and number of processing elements. Based on the situation and the value of disrupting stimulus, these parameters can be fixed to achieve the best possible trade-off between the major resource concerns. Simplicity and versatility of implementation on FPGA is one of the major points considered in this proposed architecture. Furthermore, FPGA infrastructure provides a convenient platform for implementing substantial parallel processing cores with arbitrary interconnections. On the other hand, considering the FPGA resource limitations, the total amount of available time and memory, we can fix the main parameters of this architecture to attain the best balance. Implementation of the architecture on a Xilinx Virtex-II FPGA and also analytical estimations show that the major concern for achieving the best accuracy is the limit on the total number of processing elements, not the amount of memory. This implies that we can gain more improvement in the accuracy in comparison to the amount of improvement in the speed

Scaling of CMOS feature size has long been a source of dramatic performance gains. However, the reduction in voltage levels has not been able to match this rate of scaling, leading to increasing operating temperatures and current densities. Given that most wearout mechanisms that plague semiconductor devices are highly dependent on these parameters, significantly higher failure rates are projected for future technology generations. Consequently, high reliability and fault tolerance, which have traditionally been subjects of interest for high-end server markets, are now getting emphasis in the mainstream desktop and embedded systems space. The popular solution for this has been the use of redundancy at a coarse granularity, such as dual/triple modular redundancy. In this work, we challenge the practice of coarse-granularity redundancy by identifying its inability to scale to high failure rate scenarios and investigating the advantages of finer-grained configurations. To this end, this paper presents and evaluates a highly reconfigurable multicore architecture, named StageNet (SN), that is designed with reliability as its first class design criteria. SN relies on a reconfigurable network of replicated processor pipeline stages to maximize the useful lifetime of a chip, gracefully degrading performance towards the end of life. Our results show that the proposed SN architecture can perform nearly 50% more cumulative work compared to a traditional multicore.