Branch Target Research Articles

BTIP: Branch Triggered Instruction Prefetcher Ensuring Timeliness

In CPU microarchitecture, caches store frequently accessed instructions and data by exploiting their locality, reducing memory access latency and improving application performance. However, contemporary applications with large code footprints often experience frequent Icache misses, which significantly degrade performance. Although Fetch-Directed Instruction Prefetching (FDIP) has been widely adopted in commercial processors to reduce Icache misses, our analysis reveals that FDIP still suffers from Icache misses caused by branch mispredictions and late prefetch, leaving considerable opportunity for performance optimization. Priority-Directed Instruction Prefetching (PDIP) has been proposed to reduce Icache misses caused by branch mispredictions in FDIP. However, it neglects Icache misses due to late prefetch and suffers from high storage overhead. In this paper, we proposed a branch-triggered instruction prefetcher (BTIP), which aims to prefetch Icache lines that FDIP cannot efficiently handle, including the Icache misses due to branch misprediction and late prefetch. We also introduce a novel Branch Target Buffer (BTB) organization, BTIP BTB, which stores prefetch metadata and reuses information from existing BTB entries, effectively reducing storage overhead. We implemented BTIP on the Champsim simulator and evaluated BTIP in detail using traces from the 1st Instruction Prefetching Championship (IPC-1). Our evaluation shows that BTIP outperforms both FDIP and PDIP. Specifically, BTIP reduces Icache misses by 38.0% and improves performance by 5.1% compared to FDIP. Additionally, BTIP outperforms PDIP by 1.6% while using only 41.9% of the storage space required by PDIP.

A Hardware Security Protection Method for Conditional Branches of Embedded Systems.

The branch prediction units (BPUs) generally have security vulnerabilities, which can be used by attackers to tamper with the branches, and the existing protection methods cannot defend against these attacks. Therefore, this article proposes a hardware security protection method for conditional branches of embedded systems. This method calculates the number of branch target buffer (BTB) updates every 80 clock cycles. If the number exceeds the set threshold, the BTB will be locked and prevent any process from tampering with the BTB entries, thereby resisting branch prediction analysis (BPA) attacks. Moreover, to prevent attackers from stealing the critical information of branches, the method designs the hybrid arbiter physical unclonable function (APUF) circuit to encrypt and decrypt the directions, addresses, and indexes of branches. This circuit combines the advantages of double APUF and Feed-Forward APUF, which can enhance the randomness of output response and resist machine learning attacks. If attackers still successfully tamper with the branches and disrupt the control flow integrity (CFI), this method detects tampering with the instruction codes, jump addresses, and jump directions in a timely manner through dynamic and static label comparison. The proposed method is implemented and tested on FPGA. The experimental results show that this method can achieve fine-grained security protection for conditional branches, with about 5.4% resource overhead and less than 5.5% performance overhead.

Enhancing Power Efficiency in Branch Target Buffer Design with a Two-Level Prediction Mechanism

Modern processors often face challenges when handling instructions that overwhelm the branch target buffer (BTB), leading to front-end bottlenecks. As the BTB’s capacity increases, its prediction module can become slower and power-hungry. In this paper, we introduce a straightforward yet highly effective two-level prediction mechanism to mitigate the escalating power consumption in the BTB structure, achieved by reducing the number of accesses. Our approach incorporates two main elements: M-BTB and V-BTB. M-BTB encompasses the first-level prediction mechanism and a fully associative BTB, while V-BTB houses the second-level prediction mechanism and a set-associative BTB. To implement our prediction mechanism, we optimize the traditional two-level BTB structure. The first level employs the skew mechanism, and the second level dissects the tag bits to create the Partial tag. These two levels of prediction mechanism correspond to the bank/way prediction for the two-level BTB structure. Our experimental results show that the first-stage prediction mechanism reduces M-BTB accesses by 75%, while the second-stage prediction mechanism ensures that over 98% of addresses require just zero or one way of the V-BTB to achieve a hit result. Our proposed approach achieves a remarkable 86–97% reduction in power consumption, with a minimal impact on performance and an increase in overall area efficiency.

Abstract 5625: Targeting the nonsense mediated mRNA decay pathway to prevent the degradation of highly immunogenic frameshift mutated transcripts

Abstract Introduction: Frameshift insertions/deletions (fs-indels) can lead to the generation of immunogenic non-self-peptides which contribute to anti-tumor immune responses. However, fs-indels often generate premature termination codons (PTCs), leading to mRNA degradation by the nonsense mediated mRNA decay (NMD) pathway. Importantly, some PTC-containing transcripts naturally escape NMD degradation and produce highly immunogenic neoantigens. Therefore, we hypothesize that NMD inhibition could be used to increase the cellular pool of fs-indels derived neoantigens in cancer cells, and enhance anti-tumor immunogenicity. NMD also regulates other cellular processes by degrading a subset of non-PTC-containing self-transcripts. Therefore, by focusing NMD inhibition towards the degradation of PTC-transcripts only, toxicity may be limited in a clinical setting. We investigate how targeting different NMD proteins affects the degradation of fs-transcripts across several cancers types. To assess the clinical relevance of NMD inhibition, we study how it specifically affects p53 fs-transcripts, since p53 is the most mutated gene in cancer and fs-mutant p53 has been shown to be highly immunogenic. Methods: Core NMD proteins were depleted across a panel of cell lines from different cancer types and transcriptomics, flow cytometry, qPCR and western blot were used to evaluate changes in the expression of NMD-targeted transcripts and NMD members. Proteomics and immunopeptidomics were used to study protein expression and HLA-peptide presentation arising from fs-transcripts. A Translating RNA Imaging by Coat protein Knock-off (TRICK) assay was developed to study the translation of NMD-targeted p53 mutants upon NMD inhibition. This assay can precisely quantify the translation of p53 mutant RNA upon different treatments and cellular stresses at single-molecule resolution. Results: We found an increase in expression of PTC-transcripts upon silencing of multiple NMD members. Our data suggests that some NMD members are better targets for inhibition of NMD PTC-surveillance, whilst others are more appropriate for inhibition of NMD-mediated gene expression regulation. These data support the recently proposed hypothesis that NMD is a branched pathway, where each branch targets specific subsets of transcripts depending on the cell type. These results are clinically relevant as the method by which NMD is inhibited could help optimize the generation of immunogenic neoantigens whilst minimizing toxicity. The TRICK assay has shown that inhibiting NMD surveillance reduces the degradation of p53 fs-transcripts and increases their protein expression. Conclusion: We have identified NMD pathway members that can be targeted to efficiently inhibit the surveillance role of NMD and prevent the degradation of fs-transcripts that could generate highly immunogenic neoantigens. Citation Format: Shanila Fernandez Patel, Roberto Vendramin, Danwen Qian, Yue Zhao, Lorena Ligammari, Krupa Thakkar, Jun Murai, Eva Grönroos, Jeremy Carlton, Kevin Litchfield. Targeting the nonsense mediated mRNA decay pathway to prevent the degradation of highly immunogenic frameshift mutated transcripts [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5625.

SCFI: Efficient forward fine-grained control flow integrity based on coarse-grained ISA extensions

Code reuse attack (CRA) is a severe threat to computer systems. To mitigate this threat, control-flow integrity (CFI) was proposed to restrict control-flow transfers. Processor vendors developed Instruction Set Architecture (ISA) extensions to enforce forward CFI on processors, such as Indirect Branch Tracking (IBT) developed by Intel and Branch Target Identification (BTI) developed by ARM. However, these extensions are coarse-grained and unable to provide enough security protection.We combine existing coarse-grained ISA extension with novel software modifications and propose a new fine-grained forward CFI implementation called Slot-based CFI (SCFI). SCFI consists of two software modifications: Slot Policy and ID Policy. Slot policy utilises lower bits of code address as labels to implement fine-grained CFI. ID Policy provides multi-label support to solve the destination equivalence problem. SCFI strengthens security by improving coarse-grained ISA extension to fine-grained CFI (improving Average Indirect Targets Allowed (AIA) from 752 to 6.24). SCFI costs negligible performance overhead (on average 0.78% in SPEC2006). It also has good performance in complex programs (1.12% in 471.omnetpp and 1.79% in 483.xalancbmk). In addition, SCFI only needs lightweight software instrument without hardware modification and supports complex control-flow graphs.

Selective Tibial Neurotomy for Spastic Equinovarus Foot: Operative Technique.

Spastic equinovarus foot (SEF) is a common complication of stroke and other upper motor neuron injuries. It is characterized by a plantigrade and inverted foot, often with toe curling, causing significant disability from pain, gait, and balance difficulties. Management includes physical therapy, antispasticity drugs, orthoses, chemical neurolysis, or botulinum toxin, all of which may be insufficient, sedating, or transient. Selective tibial neurotomy (STN) provides a surgical option that is effective and long-lasting. Our goal is to provide a concise description of our technique for performing the STN for treatment of SEF. We discuss the standard posterior approach with surgical variations used by other groups and a medial approach, should the posterior approach be insufficient. A posterior leg approach allows access to the tibial nerve and its branches to the bilateral gastrocnemius muscles, soleus, posterior tibialis, and extrinsic toe flexors. A medial approach is used if the toe flexors cannot be accessed sufficiently from the posterior approach. Nerve branch targets identified by preoperative functional assessment are carefully exposed and fully neurolysed distally to identify all terminal branches to each muscle of interest before neurotomy. The STN is a powerful tool for treating SEF, with an immediate and lasting effect. Approximately 80% of the target muscle should be denervated to ensure long-term efficacy while maintaining adequate function of the muscle through collateral innervation. The STN is a safe and effective outpatient procedure that can be performed by an experienced nerve surgeon to improve balance and ambulation and reduce pain for patients with SEF. Large clinical trials are necessary to further establish this underutilized procedure in the United States.

영업점 효율성과 톱니현상

영업점 효율성과 톱니현상

Relationship between mitral valve echocardiographic parameters and interventional septal reduction therapy success in patients with hypertrophic obstructive cardiomyopathy - a pilot study

Abstract Funding Acknowledgements Type of funding sources: None. Background The mitral valve (MV) has an important contribution to left ventricular outflow tract obstruction (LVOTO) in patients with hypertrophic obstructive cardiomyopathy (HOCM). Several parameters were described to be significantly different between obstructive and non-obstructive forms of HCM, like the aortomitral angle, anterior mitral leaflet (AML) and LVOT size, anterior displacement of the coaptation line, annular related parameters, etc. The redundant length of the AML beyond the coaptation point and the entire AML length are important in the decision to also approach the mitral valve when surgical septal reduction therapy (SRT) is considered, to increase the success rate. Data is scarce regarding mitral valve related predictors of interventional SRT response. This is important as certain patients have a suboptimal response regardless of the proper coronary anatomy and branch targeting. Purpose To evaluate the possible differences in certain mitral valve parameters in interventional SRT responders versus non-responders. Methods We retrospectively included in the study a number of 21 consecutive HOCM patients treated with alcohol septal ablation (ASA). All the procedures were guided with contrast echocardiography besides fluoroscopy and all patients had a proper distribution of at least 1 septal branch. They were divided in two groups- responder group (15 patients) with a non-significant residual LVOT PG (<50mmHg/>50% reduction) and non-responder group (6 patients) with a significant residual LVOT PG (>50mmHg/<50% reduction) and/or early rebound. We accounted for the residual LVOT PG evaluated by continuous Doppler at least 6 months after SRT. The groups were compared regarding LV dimensions and ejection fraction and mitral valve annulus/ leaflet size and displacement of the coaptation line, evaluated by bidimensional transthoracic echocardiography, before the intervention. Results The most hypertrophied part of the LV was the basal interventricular septum (IVS). The two groups were similar regarding age, comorbidities, LV dimensions and ejection fraction and baseline LVOT PG. Also, there was no significant difference between AML length, posterior mitral leaflet (PML) length, AML/PML, MV annulus. The redundant length of the AML was significantly lower in responders whereas anterior displacement of the coaptation point (projection of AML/projection of PML on the MV annulus) was less intense in responders and almost reached statistical significance. (Table) Interestingly, we found a baseline AML/PML projection <1 in all non-responder patients and only in 2 responder patients (13.33%) (p<0.001). Conclusion So far, significant anterior displacement of the coaptation point and the residual AML length may have an important impact on the response to ASA in HOCM patients. This pilot study could be an important start point for larger prospective studies to confirm and evaluated the prediction value of these parameters among others.

Performance, Efficiency, and Target Setting for Bank Branches: Time Series With Automated Machine Learning

Setting targets for the bank branches and distribution of annual targets to the branches and portfolio managers, quarterly, is a crucial process for decision making and strategic planning in the banking industry. Performance of the bank branches and portfolio managers are also evaluated by the quarterly divided targets to the branches and portfolio managers. In this study, the focus is on performance prediction by using state-of-art machine learning algorithms. A novel automated machine learning approach with combined algorithm selection and hyperparameter optimization is also applied for each of the branches since all the branches might have different customer segmentation and behavior. Moreover, the postconditions are executed to finalize the target calculation and distribution over the performance predictions. The study shows the success of the methodology with a successful application of 98% accuracy in the prediction and majority of branch target calculations. An end-to-end solution found to the seasonality and periodicity problem, which is the biggest problem faced by branches while achieving their goals. Also, the novel approach increases the success of branch targets by 10% in overall. The most significant innovation this study provides to the literature and practitioners is that, unlike classical studies, it solves the seasonality and periodicity problem through multiple time series modeling. The target setting procedure was employed by the largest financial institution in Turkey, Ziraat Bank, to evaluate the operating performance of its branches. The empirical study demonstrates the applicability of the proposed model in the banking sector. The outputs of the study are implemented in real life for all retail branches of Ziraat Bank. In addition, the study awarded the most innovative use of AI/ML, the most innovative project for in-house implementation related to the innovative aspect of the work, by the Global FinTech Innovation Awards 2022.

Oborové standardy a cílová kapitálová struktura v investičním rozhodování a oceňování podniku

Oborové standardy a cílová kapitálová struktura v investičním rozhodování a oceňování podniku

Integrating features from lymph node stations for metastatic lymph node detection.

Metastasis on lymph nodes (LNs), the most common way of spread for primary tumor cells, is a sign of increased mortality. However, metastatic LNs are time-consuming and challenging to detect even for professional radiologists due to their small sizes, high sparsity, and ambiguity in appearance. It is desired to leverage recent development in deep learning to automatically detect metastatic LNs. Besides a two-stage detection network, we here introduce an additional branch to leverage information about LN stations, an important reference for radiologists during metastatic LN diagnosis, as supplementary information for metastatic LN detection. The branch targets to solve a closely related task on the LN station level, i.e., classifying whether an LN station contains metastatic LN or not, so as to learn representations for LN stations. Considering that a metastatic LN station is expected to significantly affect the nearby ones, a GCN-based structure is adopted by the branch to model the relationship among different LN stations. At the classification stage of metastatic LN detection, the above learned LN station features, as well as the features reflecting the distance between the LN candidate and the LN stations, are integrated with the LN features. We validate our method on a dataset containing 114 intravenous contrast-enhanced Computed Tomography (CT) images of oral squamous cell carcinoma (OSCC) patients and show that it outperforms several state-of-the-art methods on the mFROC, maxF1, and AUC scores, respectively.

Real Image Restoration Using VAEs

Abstract— Old photos are an integral part of everybody’s life; they remind us of how one person has spent their life. As people used hard copies of photos before, those photos suffered severe degradation. This degradation in real-time images is intricate, causing thetypical restoration that might be solved through supervised learning to fail to generalize due to the domain gap between synthetic and real images. Therefore, this method uses various autoencoders to restore and colourize old images. Furthermore, this model uses a unique triplet domain translation network on real images and synthetic photo pairs. Precisely, VAEs, which are variational autoencoders, are trained to transform old pictures and clean pictures into two latent spaces. Therefore, the translation between these two latent spaces is comprehended with simulated paired data. This translation generalizes well to authentic images because the domain gap is encompassed in the close-packed latent space. Moreover, to manoeuvre numerous degradations present in one old picture, this model designs aworld branchwith a partial non-local block targeting the structured faults, like scrapes and dirt marks, and an area branch targeting unstructured faults, like noisesand fuzziness. Two branches are blended within the latent space, resulting in an improved ability to renew old pictures from numerous defects. Additionally, it applies another face refinementnetwork to revive fine details of faces within the old pictures, thus generating photos with amplified quality. Another autoencoder is encoded with colour images, and then the decoder decodes the features extracted from the encoder. Once a model is trained, testing is performed to colourize the photographs. Keywords— Degraded pictures, Variational Auto Encoders, Domain gap, Triplet domaintranslation.

Selective axonal transport through branch junctions is directed by growth cone signaling and mediated by KIF1/kinesin-3 motors.

SUMMARYDevelopment and function of nerve cells rely on the orchestration of microtubule-based transport from the cell body into distal axonal terminals. Neurons often have highly elaborate branches innervating multiple targets, but how protein or membrane cargos navigate through branch junctions to specific branch targets is unknown. Here, we demonstrate that anterograde transport of membrane vesicles through axonal branch junctions is highly selective, which is influenced by branch length and more strongly by growth cone motility. Using an optogenetic tool, we demonstrate that signaling from the growth cone can rapidly direct transport through branch junctions. We further demonstrate that such transport selectivity is differentially regulated for different vesicles and mediated by the KIF1/kinesin-3 family motors. We propose that this transport regulation through branch junctions could broadly impact neuronal development, function, and regeneration.

Bratter: An Instruction Set Extension for Forward Control-Flow Integrity in RISC-V.

In recent decades, there has been an increasing number of studies on control flow integrity (CFI), particularly those implementing hardware-assisted CFI solutions that utilize a special instruction set extension. More recently, ARM and Intel, which are prominent processor architectures, also announced instruction set extensions for CFI called branch target identification (BTI) and control-flow enhancement technology (CET), respectively. However, according to our preliminary analysis, they do not support various CFI solutions in an efficient and scalable manner. In this study, we propose Bratter, a new instruction set extension for forward CFI solutions on RISC-V. At the center of Bratter, there are Branch Tag Registers and dedicated instructions for these registers. We implemented well-known CFI solutions (i.e., branch regulation and function signature check) using Bratter to evaluate its performance. Our experimental results show that, by using Bratter, even when these two solutions work together, they impose only 1.20% and 5.99% overhead for code size and execution time, respectively.

BTB-X: A Storage-Effective BTB Organization

Many contemporary applications feature multi-megabyte instruction footprints that overwhelm the capacity of branch target buffers (BTB) and instruction caches (L1-I), causing frequent front-end stalls that inevitably hurt performance. BTB is crucial for performance as it enables the front-end to accurately resolve the upcoming execution path and steer instruction fetch appropriately. Moreover, it also enables highly effective fetch-directed instruction prefetching that can eliminate many L1-I misses. For these reasons, commercial processors allocate vast amounts of storage capacity to BTBs. This letter aims to reduce BTB storage requirements by optimizing the organization of BTB entries. Our key insight is that today's BTBs store the full target address for each branch, yet the vast majority of dynamic branches have short offsets requiring just a handful of bits to encode. Based on this insight, we organize the BTB as an ensemble of smaller BTBs, each storing offsets within a particular range. Doing so enables a dramatic reduction in storage for target addresses. We also compress tags to reduce the tag storage cost. Our final design, called BTB-X, uses an ensemble of five BTBs with compressed tags that enables it to track 2.8x more branches than a conventional BTB with the same storage budget.

Wave Reflection at the Origin of a First-Generation Branch Artery and Target Organ Protection: The AGES-Reykjavik Study.

[Figure: see text].

MemCAM: A Hybrid Memristor-CMOS CAM Cell for On-Chip Caches

Non-volatile nanoscale memory devices (such as memristors) have promised to overcome the challenges of scalability and leakage currents of CMOS based memory devices. These novel memories can be fabricated in back-end-of-the-line of any CMOS process. Currently, a lot of research is focused on investigating the benefits of memristors for associative memories. These are Content-Addressable Memories (CAM) in which search based data access takes place. Searching for a particular bit in memristor is time consuming while search in CMOS CAM zone is efficient. To combine the speed and ease of search of CMOS memory and the scalability of memristor memory, we present a novel multibit hybrid CMOS-Memristor Associative Memory Cell. The benefits of such memory cells manifest in on-chip caches - the instruction and data cache, Branch Target Buffer, and Translation Lookaside Buffer. To exemplify the benefit of the cell further, we also simulate the MemCAM as the TLB of an ARM processor and obtained upto 50% decrease in miss rates of Data TLB and upto 93% in that of Instruction TLB. Average speedup of 1.16 was also achieved on various benchmark applications of PARCSEC and MiBench suites.

An Aggressive Implementation Method of Branch Instruction Prefetch

Due to the demand in the field of industrial control, we developed a CPU core based on ARMv8m architecture [1], named TS400, and its performance is comparable to Arm Cortex-M33 [2]. The TS400 has four stages of pipelining, thus having a higher clock frequency than Cortex-M33 which has three stages. With the increase of pipeline stages, extra idle clock beats will be introduced when the pipeline is flushed, which will increase the clock cycles per instruction (CPI) value and decrease the score of CoreMark. Accurate instruction fetch and branch prediction can effectively reduce the impact of refreshing pipeline, at the cost of extra logic resources. In TS400, an aggressive branch instruction prefetch method is designed. Compared with branch prediction technology, this method does not need complex branch prediction logic and is suitable for the design of embedded CPU. The aggressive branch instruction prefetch method includes: 1) reducing the time cost of conditional branch target fetching to the minimum by taking the branch first and then confirming the branch taking result; 2) optimizing the bus control signal timing of the prefetch instruction to make the target address prefetch respond in time. The aggressive branch prefetch method reduces the impact of pipeline stall caused by the execution of conditional branch instructions as much as possible, thus achieving better running performance than Cortex-M33 at the same clock frequency, while the clock frequency performance is superior to Cortex-M33.

Shooting Down the Server Front-End Bottleneck

The front-end bottleneck is a well-established problem in server workloads owing to their deep software stacks and large instruction footprints. Despite years of research into effective L1-I and BTB prefetching, state-of-the-art techniques force a trade-off between metadata storage cost and performance. Temporal Stream prefetchers deliver high performance but require a prohibitive amount of metadata to accommodate the temporal history. Meanwhile, BTB-directed prefetchers incur low cost by using the existing in-core branch prediction structures but fall short on performance due to BTB’s inability to capture the massive control flow working set of server applications. This work overcomes the fundamental limitation of BTB-directed prefetchers, which is capturing a large control flow working set within an affordable BTB storage budget. We re-envision the BTB organization to maximize its control flow coverage by observing that an application’s instruction footprint can be mapped as a combination of its unconditional branch working set and, for each unconditional branch, a spatial encoding of the cache blocks around the branch target. Effectively capturing a map of the application’s instruction footprint in the BTB enables highly effective BTB-directed prefetching that outperforms the state-of-the-art prefetchers by up to 10% for equivalent storage budget.

In vivo measurements of medial branch nerve depth and adjacent osseous structures for ablation of facet-related back pain: Predictors for patient candidacy

BackgroundMedial branch (MB) targeting during RF ablation for facetogenic back pain is usually performed with flouroscopic guidance yet no specific measurements on the target depth have been published. In order to understand candidacy for other potential ablation methods, we sought to determine the actual MB depth and measurements of adjacent osseous structures. MethodsCT scans without contrast of the lumbar spine performed in the supine position were retrospectively analyzed in 100 patients. Axial slices less than or equal to 2.5 mm with sagittal and coronal reformations were evaluated. The following distances were measured bilaterally at the L2-L5 levels: The depth from the skin to the MB nerve (anatomic target for RF ablation) at a 15° angulation, the smallest width of the pedicle, and the length, height and width of the transverse process. Age, gender, weight, height, and BMI were correlated with the above measurements. ResultsThe average distance and 95% CI from skin-to-MB in mm at a 15°angle to the skin increased as the lumbar level increased measuring 64.4 (62.4–66.5) at L2, 72.0 (69.7–74.3) at L3, 79.2 (76.9–81.6) at L4, and 79.1 (76.7–81.5) at L5. The average thickness of the pedicles also increased as the lumbar level increased measuring 9.2 mm at L2 and 16.1 mm at L5. Body weight, lumbar level, and female gender were associated with increased MB depth. Taller stature was associated with more superficial MB depth. We eliminated mild interaction effects between height, weight, and gender by substituting BMI for height and weight without affecting r2. Linear regression revealed the following equation: MB Depth (mm) = 2.2*BMI + 4.9*lumbar vertebral level + 3.6 (if female) – 5.4, which fit the data well (P<0.001, r2 = 0.60). ConclusionsOur results demonstrate that the MB resides 107 mm or less in depth when measured at a 15° angulation from the skin in > 95% of patients and the distance increases as the lumbar level increases.