As evidence mounts that the cardiac-sympathetic nervous system reacts to challenging cognitive settings, we ask if these responses are epiphenomenal companions or if there is evidence suggesting a more intertwined role of this system with cognitive function. Healthy male and female human participants performed an approach-avoidance paradigm, trading off monetary reward for painful electric shock, while we recorded simultaneous electroencephalographic and cardiac-sympathetic signals. Participants were reward sensitive but also experienced approach-avoidance "conflict" when the subjective appeal of the reward was near equivalent to the revulsion of the cost. Drift-diffusion model parameters suggested that participants managed conflict in part by integrating larger volumes of evidence into choices (wider decision boundaries). Late alpha-band (neural) dynamics were consistent with widening decision boundaries serving to combat reward sensitivity and spread attention more fairly to all dimensions of available information. Independently, wider boundaries were also associated with cardiac "contractility" (an index of sympathetically mediated positive inotropy). We also saw evidence of conflict-specific "collaboration" between the neural and cardiac-sympathetic signals. In states of high conflict, the alignment (i.e., product) of alpha dynamics and contractility were associated with a further widening of the boundary, independent of either signal's singular association. Cross-trial coherence analyses provided additional evidence that the autonomic systems controlling cardiac-sympathetics might influence the assessment of information streams during conflict by disrupting or overriding reward processing. We conclude that cardiac-sympathetic control might play a critical role, in collaboration with cognitive processes, during the approach-avoidance conflict in humans.

FAO Director-General José Graziano da Silva will be away from Rome during the next few weeks. During this period he will be involved in a range of [...]

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Last summer, news reports of orcas deliberately tearing the propellers off of yachts in the Strait of Gibraltar thrilled observers who were eager to cast these intelligent and social pack hunters as class warriors striking a blow for the “common mammals” against the one percent. That turned out to be wishful thinking, according to guest Lori Marino, a biopsychologist who studies whale and dolphin intelligence. She told us that these six-ton whales were just having fun—if they wanted to harm the occupants of those boats, we’d know it.  Even so, these encounters are becoming a predictable seasonal occurrence between the months of May and August: A 50-foot charter vessel sank after its hull and rudder were damaged in an orca encounter near the Strait of Gibraltar on May 12. So here again is our episode on the perils of assigning human motives to wild animals, featuring Marino and Smithsonian assistant digital science editor Carlyn Kranking. This episode was originally released in September 2023.   Dr. Marino invites you to learn more about The Whale Sanctuary Project at their site (https://whalesanctuaryproject.org/about-the-whale-sanctuary-project/) . You can also see Dr. Marino in the documentary films Blackfish (2013), Unlocking the Cage (2016), and Long Gone Wild (2019). Find prior episodes of our show here (https://www.smithsonianmag.com/podcast/) . And read the transcript of this episode here (https://www.smithsonianmag.com/science-nature/are-wild-animals-really-just-like-us-180982939/) . There’s More to That (https://www.smithsonianmag.com/podcast) is a production of Smithsonian magazine and PRX Productions. From the magazine, our team is Chris Klimek, Debra Rosenberg and Brian Wolly. From PRX, our team is Jessica Miller, Adriana Rosas Rivera, Genevieve Sponsler, Rye Dorsey, and Edwin Ochoa. The Executive Producer of PRX Productions is Jocelyn Gonzales. Fact-checking by Stephanie Abramson. Episode artwork by Emily Lankiewicz. Music by APM Music.

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DDR5 DIMM Architectures The DDR5 generation of Double Data Rate DRAM memories has experienced rapid adoption in recent years. In particular, the JEDEC-defined DDR5 Dual Inline Memory Module (DIMM) cards have become a mainstay for systems looking for high-density, high-bandwidth, off-chip random access memory[1]. Within a short time, the DIMM architecture evolved from an interconnected hierarchy of only SDRAM memory devices (UDIMM[2]) to complex subsystems of interconnected components (RDIMM/LRDIMM/MRDIMM[3]). DIMM Designs and Popular Verification Use Cases The growing complexity of the DIMMs presented a challenge for pre-silicon verification engineers who could no longer simply validate against single DDR5 SDRAM memory models. They needed to consider how their designs would perform against DIMMs connected to each channel and operating at gigahertz clock speeds. To address this verification gap, Cadence developed DDR5 DIMM Memory Models that encapsulated all of the architectural complexities presented by real-world DIMMs based on a robust, easy-to-use, easy-to-debug, and easy-to-reconfigure methodology. This memory-subsystem-in-a-single-instance model has seen explosive adoption among the traditional IP Developer and SOC Integrator customers of Cadence Memory Models. The Cadence DIMM models act as a single unit with all of the relevant DIMM components instantiated and interconnected within, and with all AC/Timing parameters among the various components fully matched out-of-the-box, based on JEDEC specifications as well as datasheets of actual devices in the market. The typical use-case for the DIMM models has been where the DUT is a DDR5 Memory Controller + PHY IP stack, and the validation plan mandated compliance with the JEDEC standards and Memory Device vendor datasheets. Unique Use Case for the DIMM Discrete Component Models Although the Cadence DIMM models have enjoyed tremendous proliferation because of their cohesive implementation and unified user API, the actual DIMM Models are built on top of powerful, flexible discrete component models, each of which was designed to stand on its own as a complete SystemVerilog UVM-based VIP. All of these discrete component models exist in the Cadence VIP Catalog as standalone VIPs, complete with their own protocol compliance checking capabilities and their own configuration mappings comprehensively modeling individual AC/Timing parameters. Because of this deliberate design decision, the Cadence DIMM Discrete Component Models can support a unique use-case scenario. Some users seek to develop IC Designs for the various DIMM components. Such users need verification environments that can model the individual components of a DIMM and allow them the option to replace one or another component with their Component Design IP. They can then validate that their component design is fully compatible with the rest of the components on the DIMM and meets the integrity of the overall DIMM compliance with JEDEC standards or Memory Vendor datasheets. The Cadence Memory VIP portfolio today includes various examples that demonstrate how customers can create DIMM “wrappers” by selecting from among the available DIMM discrete component models and “stitching” them together to build their own custom testbench around their specific Component Design IP. A Solution for Unique Component Scenarios The Cadence DDR5 DIMM Memory Models and DIMM Discrete Component Models can provide users with a flexible approach to validating their specific component designs with a fully populated pre-silicon environment. Augmented Verification Capabilities When the DIMM “wrapper” model is augmented with the Cadence DFI VIP[4] that can simulate an MC+PHY stack and offers a SystemVerilog UVM test API to the verification engineer, the overall testbench transforms into a formidable pre-silicon validation vehicle. The DFI VIP is designed as a combination of an independent DFI MC VIP and a DFI PHY VIP connected to each other via the DFI Standard Interface and capable of operating seamlessly as a single unit. It presents a UVM Sequence API to the user into the DFI MC VIP with the Memory Interface of the PHY VIP connected to the DIMM “wrapper” model. With this testbench in hand, the user can then fully take advantage of the UVM Sequence Library that comes with the DFI VIP to enable deep validation of their Component Design inside the DIMM “wrapper” model. Verification Capabilities Further Enhanced A possible further enhancement comes with the potential addition of an instance of the Cadence DIMM Memory Model in a Passive Monitor mode at the DRAM Memory Interface. The DIMM Passive Monitor consumes the same configuration describing the DIMM “wrapper” in the testbench, and thus can act as a reference model for the DIMM wrapper. If the DIMM Passive Monitor responds successfully to accesses from the DFI VIP, but the DIMM wrapper does not, then it exposes potential bugs in the DUT Components or in the settings of their AC/Timing parameters inside the DIMM wrapper. Debuggability, Interface Visibility, and Protocol Compliance One of the key benefits of the DIMM Discrete Component Models that become manifest, whether in terms of the unique use-case scenario described here, or when working with the wholly unified DDR5 DIMM Memory Models, is the increased debuggability of the protocol functionality. The intentional separation of the discrete components of a DIMM allows the user to have full visibility of the memory traffic at every datapath landmark within a DIMM structure. For example, in modeling an LRDIMM or MRDIMM, the interface between the RCD component and the SDRAM components, the interface between the RCD component and the DB components, and the interface between the SDRAM components and the DB components—all are visible and accessible to the user. The user has full access to dump the values and states of the wire interconnects at these interfaces to the waveform viewer and thus can observe and correlate the activity against any protocol violations flagged in the trace logs by any one or more of the DIMM Discrete Component Models. Access to these interfaces is freely available when using the DIMM Discrete Component Models. On the unified DDR5 DIMM Memory Models, a feature called Debug Ports enables the same level of visibility into the individual interconnects amidst the SDRAM components, RCD components, and DB components. When combined with the Waveform Debugger[5] capability that comes built-in with the VIPs and Memory Models offered by Cadence and used with the Cadence Verisium Debug[6] tool, the enhanced debuggability becomes a powerful platform. With these debug accesses enabled, the user can pull out transaction streams, chip state and bank state streams, mode register streams, and error message streams all right next to their RTL signals in the same Verisium Debug waveform viewer window to debug failures all in one place. The Verisium Debug tool also parses all of the log files to probe and extract messages into a fully integrated Smart Log in a tabbed window fully hyperlinked to the waveform viewer, all at your fingertips. A Solution for Every Scenario Cadence's DDR5 DIMM Memory Models and DIMM Discrete Component Models , partnered with the Cadence DFI VIP, can provide users with a robust and flexible approach to validating their designs thoroughly and effectively in pre-silicon verification environments ahead of tapeout commitments. The solution offers unparalleled latitude in debuggability when the Debug Ports and Waveform Debugger functions of the Memory Models are switched on and boosted with the use of the Cadence Verisium Debug tool. [1] Shyam Sharma, DDR5 DIMM Design and Verification Considerations , 13 Jan 2023. [2] Shyam Sharma, DDR5 UDIMM Evolution to Clock Buffered DIMMs (CUDIMM) , 23 Sep 2024. [3] Kos Gitchev, DDR5 12.8Gbps MRDIMM IP: Powering the Future of AI, HPC, and Data Centers , 26 Aug 2024. [4] Chetan Shingala and Salehabibi Shaikh, How to Verify JEDEC DRAM Memory Controller, PHY, or Memory Device? , 29 Mar 2022. [5] Rahul Jha, Cadence Memory Models - The Gold Standard , 15 Apr 2024. [6] Manisha Pradhan, Accelerate Design Debugging Using Verisium Debug , 11 Jul 2023.

I've heard lots of people asking for a way to generate Makefiles for Specman code, and it seems there are some who don't use "irun" which takes care of this automatically. So I wrote this little utility to build a basic Makefile based on the compiled and loaded e code.

It's really easy to use: at any time load the snmakedeps.e into Specman, and use "write makefile <name> [-ignore_test]".
This will dump a Makefile with a set of variables corresponding to the loaded packages, and targets to build any compiled modules.
Using -ignore_test will avoid having the test file in the Makefile, in case you switch tests often (who doesn't?).

It also writes a stub target so you can do "make stub_ncvlog" or "make stub vhdl" etc.

The targets are pretty basic, I thought it was more useful to #include this into the main Makefile and define your own more complex targets / dependencies as required.

The package uses the "reflection" facility of the e language, which is now documented since Specman 8.1, so you can extend this utility if you want (please share any enhancements you make).

 Enjoy! :-)

Steve.

Hi All.

I put together a small Perl script to generate vr_ad register definitions from SPIRIT (IP-XACT) XML.
If you've got not idea what IP-XACT is, have a look here www.spiritconsortium.org/, then start pestering your design manager to use it :-)

The script can filter out registers and override R/W access types if needed.

An example XML file is included with the package so that you can play with it, and there's a detailed README.txt as well.

Here's an example of the generated e code:

// Automatically generated from xdmac.xml
// DO NOT EDIT, or your changes may be lost
<'

import vr_ad/e/vr_ad_top;

// Component = XDMAC
// memoryMap = xdmac
extend vr_ad_map_kind : [XDMAC];

// addressBlock = dma_eth
extend vr_ad_reg_file_kind : [DMA_ETH];

extend DMA_ETH vr_ad_reg_file {
  keep size == 20;
  keep addressing_width_in_bytes == 4;
};

// Register = command
// Reset    = 0x00
reg_def COMMAND DMA_ETH 0x0 {
  // Field resv3 = command[31:29]
  reg_fld resv3 : uint(bits:3) : R : 0 : cov ;
  // Field transfer_size = command[28:19]
  reg_fld transfer_size : uint(bits:10) : RW : 0 : cov ;
  // Field dma_transfer_target = command[18:14]
  reg_fld dma_transfer_target : uint(bits:5) : RW : 0 : cov ;
  // Field resv2 = command[13:10]
  reg_fld resv2 : uint(bits:4) : R : 0 : cov ;
  // Field transmit_receive = command[9:9]
  reg_fld transmit_receive : uint(bits:1) : RW : 0 : cov ;
  // Field resv1 = command[8:5]
  reg_fld resv1 : uint(bits:4) : R : 0 : cov ;
  // Field dest_address_enable = command[4:4]
  reg_fld dest_address_enable : uint(bits:1) : RW : 0 : cov ;
  // Field source_address_enable = command[3:3]
  reg_fld source_address_enable : uint(bits:1) : RW : 0 : cov ;
  // Field word_size = command[2:0]
  reg_fld word_size : uint(bits:3) : R : 0 : cov ;
};

// Register = queue_exec
// Reset    = 0x00
reg_def QUEUE_EXEC DMA_ETH 0x10 {
  // Field resv = queue_exec[31:1]
  reg_fld resv : uint(bits:31) : R : 0 : cov ;
  // Field exec = queue_exec[0:0]
  reg_fld exec : uint(bits:1) : RW : 0 : cov ;
};

extend XDMAC vr_ad_map {
  dma_eth : DMA_ETH vr_ad_reg_file;

  post_generate() is also {
    add_with_offset(0x00, dma_eth);
    dma_eth.reset();
  };
}
'>

Any comments, please feed them back to me so I can enhance the script.
Note that this forum forces me to post a .zip file rather than .tgz, please be careful to unpack the file under Linux, not Windows, else the DOS linefeeds will corrupt the Perl and XML files.

Steve

As noted in white papers, posts on the Team Specman Blog, and the Specman documentation, IntelliGen is a totally new stimulus generator than the original "Pgen" and, as a result, there is some amount of effort needed to migrate an existing verification environment to fully leverage the power of IntelliGen.  One of the main steps in migrating code is running the linters on your code and adressing the issues highlighted. 

Included below is a simple utility you can include in your environment that allows you to collect some valuable statistics about your code base to allow you to better gauge the amount of work that might be required to migrate from Pgen to IntelliGen.  The ICFS statistics reported are of particular benefit as the utility not only identifies the approximate number of ICFSs in the environment, it also breaks the total number down according to generation contexts (structs/units and gen-on-the-fly statements) allowing you to better focus your migration efforts. 

IMPORTANT: Sometimes a given environment can trigger a large number of IntelliGen linting messages right off the bat.  Don't let this freak you out!  This does not mean that migration will be a long effort as quite often some slight changes to an environment remove a large number of identified issues.  I recently encountered a situation where a simple change to three locations in the environment, removed 500+ ICFSs!

The methods included in the utility can be used to report information on the following:
- Number of e modules
- Number of lines in the environment (including blanks and comments)
- Number and type of IntelliGen Guidelines linting messages
- Number of Inconsistently Connected Field Sets (ICFSs)
- Number of ICFS contexts and how many ICFSs per context
- Number of soft..select overlays found in the envioronment
- Number of Laces identified in the environment

To use the code below, simply load it before/after loading e-code and then
you can execute any of the following methods:

- sys.print_file_stats()             : prints # of lines and files
- sys.print_constraint_stats()   : prints # of constraints in the environment
- sys.print_guideline_stats()    : prints # of each type of linting message
- sys.print_icfs_stats()            : prints # of ICFSs, contexts and #ICFS/context
- sys.print_soft_select_stats() : prints # of soft select overlay issues
- sys.print_lace_stats()           : *Only works for SPMNv6.2s4 and later* prints # of laces identified in the environment

Each of the above calls to methods produces it's own log files (stored in the current working directory) containing relevant information for more detailed analysis.
- file_stats_log.elog : Output of "show modules" command
- constraint_log.elog : Output of the "show constraint" command
- guidelines_log.elog : Output of "gen lint -g" (with notification set to MAX_INT in order to get all warnings)
- icfs_log.elog       : Output of "gen lint -i" command
- soft_select_log.elog: Output of the "gen lint -s" command
- lace_log.elog       : Output of the "show lace" command

Happy generating!

Corey Goss

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