BrainChip AKD1000 SNN AI SoC will get Raspberry Pi and x86 improvement kits

BrainChip has launched two improvement kits for its Akida AKD1000 neuromorphic processor based mostly on Raspberry Pi and an Intel (x86) mini PC as a way to allow companions, massive enterprises, and OEMs to start testing and validation of the Akida chip.

BrainChip Akida neural depends on spiking neural networks (SNN) which allow high-performance, real-time inference at ultra-low energy, notably a lot decrease energy than conventional AI chips counting on CNN (convolutional neural community) expertise.

Akida Improvement Package based mostly on Raspberry Pi CM4

Brainchip Akida Raspberry Pi Devkit


  • SoM – Raspberry Pi CM4 or CM4 Lite with
    • SoC: Broadcom BCM2711C0 quad-core ARM Cortex-A72 (ARMv8-A) 64-bit @ 1.5GHz plus Broadcom VideoCore VI GPU
    • RAM – 1GB, 2GB, 4GB, or 8GB LPDDR4 SDRAM
    • Storage – MicroSD card for CM4 Lite, or 2GB to 32GB eMMC for CM4
    • Networking – Optionally available 2.4 GHz and 5 GHz 802.11b/g/n/ac Wi-Fi, Bluetooth 5.0 LE, Gigabit Ethernet PHY
  • Service board –  Official Raspberry Pi Compute Module 4 IO board with PCIe slot, 2x DSI, 2x CSI, 2x HDMI
  • AI accelerator – Mini PCIe card with Brainchip Akida AKD1000 AI chip
  • Dimensions – 20.06 x 10.5 x 3.7cm

Raspberry Pi Compute Module IO Board AI accelerator card

The equipment is simply comprised of off-the-shelf components (RPi CM4 + provider board) housed in an enclosure with the corporate’s Akida AI accelerator card.

Akida Improvement Package based mostly on Shuttle PC

Brainchip Akida Shuttle PCSpecs:

  • SoC help – LGA1200 socket for Tenth-gen Intel Core i9/i7/i5/i3, Pentium Gold, and Celeron “Comet Lake-S” processors with as much as 65W TDP.  Consists of Heatpipe cooling system with two followers
  • System Reminiscence –  2x 260-pin SO-DIMM slot for DDR4-2933 /2666 as much as 2x 32 GB RAM
  • Storage
    • 1x 2.5-inch bay for SATA onerous disk or SSD, max. 9.5 mm
    • 1x M.2-2280M slot (helps PCIe x4 NVMe or SATA)
  • Video Output – HDMI 2.0a, VGA
  • Audio – 2x 3.5mm audio jacks for line out and mic
  •  Networking
    • Gigabit Ethernet RJ45 port
    • 1x M.2-2230AE for an non-obligatory WLAN card
  • USB – 4x USB 3.2 Gen1, 4x USB 2.0, 1x USB 2.0 inner USB stick
  • Serial – Optionally available RS232 COM port
  • Enlargement – 1x PCI Specific X16 v3.0 slot
  • AI accelerator – Mini PCIe card with Brainchip Akida AKD1000 AI chip
  • Energy Provide – Exterior 180 W / 19.5 V energy adapter
  • Dimensions – 25 x 20 x 7.85cm

Home windows 10 and Linux (64-bit)

Mini PC Brainchip Akida mini-PCIe AI accelerator cardThat improvement equipment is principally Shuttle XH410G mini PC with Braindchip Akida mPCIe card.

BrainChip AKD1000 NSoC and software program help


Brainchip AKD1000  Akida Neuromorphic processorThe neuromorphic SoC (NSoC) emulates 1.2 million neurons and 10 billion synapses with Akida neuron cloth and presents the next key options and interfaces:

  • M-Class SPU with FPU and DSP
  • Reminiscence I/F – LPDDR4
  • Storage – SPI flash for boot/storage
  • Akira Neuro Cloth – 80 NPUs, digital logic with 8MB SRAM
  • Knowledge enter interfaces
    • PCIe 2.1 x2
    • USB 3.0
    • I3C, I2C, UART, JTAG
  • Knowledge processing – Pixel occasion converter; SW data-event encoder; multivariable digital information; sound, strain, temperature… information dealing with
  • Multi-chip growth by way of further PCIe 2.1 2-lane root complicated with as much as 64 “daisy-chained” units
  • Course of – 28nm TSMC

The event kits listed above embrace the Meta TF Software program Improvement Surroundings, a User Guide, and examples of Akida Fashions. Interviewed by EETimes, Anil Mankar, BrainChip co-founder and chief improvement officer, explains that people who find themselves accustomed to the TensorFlow or Keras API can take their current utility, community, dataset, and run it on Akida {hardware} after going by way of quantization-aware coaching, and measure the facility themselves. So there’s no want to grasp precisely how SNN works.

Brainchip Akida efficiency
MAC operations required for object classification inference – Darkish blue is CNN within the non-event area, gentle blue is occasion area/Akida, inexperienced is occasion area with additional exercise regularization. Supply: BrainChip by way of EETimes

The best way they examine the answer to CNN is attention-grabbing as they report the quantity MAC operation wanted per inference as a substitute of direct mW values. But the EETimes article additionally offers some energy consumption numbers:

… a keyword-spotting mannequin working on the Akida improvement board after 4-bit quantization consumed as little as 37 µJ per inference (or 27,336 inferences per second per Watt). Prediction accuracy was 91.3 p.c, and the chip was slowed to five MHz to realize the noticed efficiency.

Availability and Value

Neuromorphic chips based on spiking neural networks are still emerging technology, and the price of the development kits reflects this with the Raspberry Pi devkit going for $4,995 and the Shuttle PC equipment for $9,995. Extra particulars could be discovered on the BrainChip website. Different corporations engaged on SNN AI chips embrace Innatera and Prophesee.

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