CV22A36-A0-RH - Ambarella CV22 SoC AI Vision Processor
料号： CV22A36-A0-RH ✓ 量产中| 数量 | 单价 | 小计 |
|---|---|---|
| 1 | $48.5 | $48.50 |
| 10 | $44.2 | $442.00 |
| 100 | $39.8 | $3,980.00 |
| 500 | $35.6 | $17,800.00 |
| 1,000 | $32.4 | $32,400.00 |
CV22A36-A0-RH 概述
A CVflow SoC (Computer Vision System-on-Chip) is a specialized AI vision processor that integrates a CPU subsystem, a hardware-accelerated CNN/DNN engine, an Image Signal Processor (ISP), and a video encoder/decoder into one chip. CVflow is Ambarella's proprietary architecture optimized for low-power, high-throughput computer-vision inference. Within the broader SoC taxonomy, a CVflow chip sits in the hierarchy: CVflow AI vision processor -> computer vision SoC -> AI accelerator SoC -> system-on-chip -> semiconductor. This category is distinct from general-purpose GPUs and CPUs because it is purpose-built for parallel convolution math, ISP pipelines, and low-latency video encoding.
Key differentiating features include hardware-accelerated CNN/DNN inference for detection, classification, and tracking; an integrated ISP pipeline supporting high-resolution sensors; 4K-capable H.265/H.264 video encoding; and a Linux SDK that supports the Caffe, PyTorch, and TensorFlow model frameworks. According to Ambarella family documentation, the CV22 family is engineered for power-constrained edge-AI cameras, where local inference offload to the cloud is impractical for latency or privacy reasons.
Architecturally, the CV22 uses a multi-core ARM CPU complex combined with Ambarella's CVflow CNN/DNN fabric. This heterogeneous compute approach allows the ARM cores to handle control flow, OS scheduling, and pre/post-processing while the CVflow fabric executes convolution layers in parallel, achieving high TOPS/W efficiency compared with general-purpose inference accelerators.
Typical applications include AI dashcams, advanced driver-assistance systems (ADAS) front-facing cameras, smart traffic cameras, industrial machine-vision systems, body-worn cameras, and AI-enabled IoT security cameras. The CV22A36-A0-RH is particularly suited to professional and industrial smart-camera deployments where both image quality and on-device inference accuracy matter.
When designing with this part, ensure the PCB thermal design accommodates the SoC's power envelope and that the SDK's BSP matches the target sensor and memory configuration. Verified reference designs and SDK documentation should be consulted before finalizing the PCB layout, since the BGA package requires controlled-impedance routing for high-speed sensor and DRAM interfaces.
This page synthesizes distributor availability, family-level alternative MPNs, and practical design guidance beyond the bare datasheet, helping engineers evaluate the CV22A36-A0-RH for their edge-AI camera application.
直接替换替代料： CV22A36-A0-RH ——相同封装，引脚兼容。需要 PCB 改动的不同封装料号已排除。
快速对比工具——选择替代料号进行并排对比：
CV22AQ36-A0-RH
✅ 直接替换📋 参考替代料（不在目录中）
CV22AQ36-A1-RH
✅ 直接替换📋 参考替代料（不在目录中）
CV22AX36-A1-RH
✅ 直接替换📋 参考替代料（不在目录中）
CV22A36-A1-RH
✅ 直接替换 ⚠️ 规格未核验📋 参考替代料（不在目录中）
CV25AQ36-A0-RH
✅ 直接替换 ⚠️ 规格未核验📋 参考替代料（不在目录中）
CV22A36-A0-RH 最大额定值与电气特性
| Product Family | CV22 (CVflow AI Vision SoC) |
| Part Number | CV22A36-A0-RH |
| AI Architecture | Ambarella CVflow (CNN/DNN hardware accelerator) |
| Image Signal Processor (ISP) | Integrated multi-channel ISP (family-level) |
| Video Encoder | H.265 / H.264 4K-capable encoder (family-level) |
| Supported ML Frameworks | Caffe, PyTorch, TensorFlow (CNN toolkit porting) |
| 工作温度 | Industrial grade (extended range) |
| 安装类型 | Surface Mount (BGA) |
| RoHS 状态 | Compliant |
| Application Domain | Edge AI vision cameras |
| Target Markets | Automotive, security, IoT, industrial, dashcam, ADAS |
| Software Support | Linux SDK with CVflow CNN toolkit |
CV22A36-A0-RH standard 引脚配置指南
CV22A36-A0-RH（standard 封装）的引脚配置。 该数字 IC 包含 GPIO、通信接口（UART、SPI、I2C）和电源引脚。请参阅制造商数据手册以了解复用引脚功能和配置选项。对嵌入式系统设计和 PCB 布局至关重要。
暂无详细引脚数据： CV22A36-A0-RH.
请参阅 数据手册 以查看完整引脚配置。
典型应用
CV22A36-A0-RH 适用于 6 应用s: AI Dashcam / Smart Driving Recorder, ADAS Front-Facing Camera, Smart Traffic / ANPR Camera, AI Security / Surveillance Camera, Body-Worn / Law-Enforcement Camera, Industrial Machine Vision.
AI Dashcam / Smart Driving Recorder
The CV22A36-A0-RH is a strong fit for next-generation AI dashcams that need 4K H.265 video capture combined with real-time detection of vehicles, lane markings, and traffic signs. The integrated CVflow CNN/DNN fabric runs detection and classification on-device, eliminating the latency of cloud inference while the H.265 encoder preserves high-quality evidence footage. The BGA package and Linux SDK allow automotive Tier-1 suppliers to integrate the SoC into compact form-factor dashcams with minimal external components. Compared with a discrete ISP-plus-AI-accelerator approach, the CV22A36-A0-RH reduces BOM and power, enabling fan-less dashcam enclosures.
推荐
ADAS Front-Facing Camera
For ADAS front-facing cameras, the CV22A36-A0-RH delivers an integrated ISP, hardware-accelerated CNN/DNN inference, and 4K H.265 encoding on a single die. This combination is critical for forward-collision warning, lane-departure detection, and traffic-sign recognition at highway speeds. The CVflow fabric executes the CNN layers in parallel with the ARM cores handling control flow, achieving the inference throughput required for 30+ fps detection on QHD/4K input streams. The extended-temperature silicon revision targets automotive-grade thermal envelopes.
推荐
Smart Traffic / ANPR Camera
Smart traffic and automatic number-plate recognition (ANPR) cameras benefit from the CV22A36-A0-RH's combination of high-quality ISP and CVflow CNN/DNN accelerator. The SoC can run license-plate detection and OCR inference at the edge while simultaneously encoding H.265 video for archival or streaming. Its low-power envelope allows pole-mount enclosures without active cooling, and the Linux SDK simplifies integration with traffic-management back-ends. Compared with server-side inference, on-camera CVflow processing reduces bandwidth and improves response time.
推荐
AI Security / Surveillance Camera
For professional AI security cameras, the CV22A36-A0-RH delivers intrusion-detection and people/vehicle classification on-device via its CVflow CNN/DNN fabric. The integrated ISP supports high-resolution sensors for low-light imaging, and the H.265 encoder minimizes storage bandwidth by 30-50% versus H.264 at equivalent quality. On-camera inference also reduces false alarms by filtering events locally before network upload, a critical advantage in large-scale surveillance deployments.
推荐
Body-Worn / Law-Enforcement Camera
Body-worn cameras for law enforcement and security personnel require compact form factors, low power, and tamper-resistant evidence capture. The CV22A36-A0-RH integrates the ISP, AI accelerator, and H.265 encoder in a single BGA, enabling slim body-worn enclosures. CVflow-based on-device classification can tag events (e.g. 'detected loud noise', 'identified person') for faster post-incident review. Battery life benefits from the SoC's low-power CVflow inference.
推荐
Industrial Machine Vision
Industrial machine-vision systems benefit from the CV22A36-A0-RH's combination of high-quality ISP, hardware CNN/DNN fabric, and 4K H.265 encoding. The SoC can run defect-detection, part-classification, and barcode/OCR inference directly on the production line without sending raw video to a server. The CVflow fabric's low-power-per-TOPS advantage enables fan-less industrial enclosures in dusty factory environments. The Linux SDK simplifies integration with PLCs and SCADA systems.
推荐
推荐产品汇总
工程参考数据： CV22A36-A0-RH ——对比、设计指导和合规信息。
选型指南
与替代料对比
| 参数 | 本产品 | CV22AQ36-A0-RH | CV22AQ36-A1-RH | CV22AX36-A1-RH | CV22A36-A1-RH | CV25AQ36-A0-RH |
|---|---|---|---|---|---|---|
| Brand | Ambarella | Ambarella | Ambarella | Ambarella | Ambarella | Ambarella |
| Package | BGA (family-standard) | BGA (CV22 family) - same family package | BGA (CV22 family) - same family package | BGA (CV22 family) - same family package | BGA (CV22 family) - same family package | BGA (CV25 family) - higher-tier family package |
| AI Architecture | CVflow CNN/DNN fabric | CVflow CNN/DNN fabric | CVflow CNN/DNN fabric | CVflow CNN/DNN fabric | CVflow CNN/DNN fabric | CVflow CNN/DNN fabric (higher tier) |
| Silicon Revision | A0 | A0 | A1 | A1 | A1 | A0 |
| Maximum Video Resolution | 4K H.265/H.264 (family) | 4K H.265/H.264 | 4K H.265/H.264 | 12MP30 (enhanced) | 4K H.265/H.264 | 8K-class (higher tier) |
| CNN Throughput | Family baseline | Same family baseline | Same family baseline | Same family baseline (X-tier feature) | Same family baseline | +~30% vs CV22 (higher tier) |
| Supported ML Frameworks | Caffe, PyTorch, TensorFlow | Caffe, PyTorch, TensorFlow | Caffe, PyTorch, TensorFlow | Caffe, PyTorch, TensorFlow | Caffe, PyTorch, TensorFlow | Caffe, PyTorch, TensorFlow |
| Software Support | Linux SDK + CVflow toolkit | Linux SDK + CVflow toolkit | Linux SDK + CVflow toolkit | Linux SDK + CVflow toolkit | Linux SDK + CVflow toolkit | Linux SDK + CVflow toolkit |
| Target Market Tier | Mainstream edge-AI | Mainstream edge-AI (Q-tier) | Mainstream edge-AI (Q-tier) | Mainstream edge-AI (X-tier) | Mainstream edge-AI | Premium edge-AI (CV25 family) |
关键差异点
- Integrated ISP + CVflow CNN + 4K encoder on a single die （对比 CV22AX36-A1-RH)
- Lower-tier price point within the CV22 family （对比 CV25AQ36-A0-RH)
- Proven Ambarella CVflow SDK with broad framework support （对比 CV22A36-A1-RH)
设计说明
Estimated: the CV22A36-A0-RH BGA package dissipates several watts under CNN inference plus 4K encoding. Use at least a 4-layer PCB with a continuous ground/power pour directly beneath the BGA, and consider a small heat-spreader or thermal pad connecting the BGA topside ground balls to the enclosure. Avoid placing heat-sensitive analog components (e.g. crystal, LDO) within 5 mm of the BGA. Always verify thermal design against the official Ambarella reference design.
Estimated: the CV22A36-A0-RH BGA land pattern requires controlled-impedance routing for the DRAM interface (typically DDR4/LPDDR4), MIPI CSI-2 sensor lanes, and high-speed serial outputs. Use 100-ohm differential impedance for MIPI pairs and match trace lengths within the budget specified in the Ambarella hardware manual. Fan-out from the BGA with vias-in-pad is recommended for breakout routing; design the stack-up with reference planes adjacent to high-speed signal layers to control impedance and EMI.
Common pitfalls when designing with the CV22A36-A0-RH include: (1) using a non-NDA pinout sketch from a third-party site and missing hidden balls, leading to PCB respins; (2) under-budgeting power-rail decoupling, which can cause CNN-inference brown-outs; (3) mis-aligning the SDK BSP version with the silicon revision (A0 vs A1) which causes boot failures; and (4) sourcing from non-franchised stockists without verifying lot/date codes. Always start from the official Ambarella reference design and verify the BSP supports the chosen silicon revision before committing to layout.
合规信息
RoHS-compliant per Ambarella family documentation. AEC-Q100 status not applicable to this generic consumer/industrial AI vision SoC; for automotive-grade variants consult Ambarella's automotive-qualified CV family members. REACH, halogen-free, and conflict-mineral specifics were not confirmed in the verified web data and are listed as 'unknown'.
